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Steinfeldt J, Wild B, Buergel T, Pietzner M, Upmeier Zu Belzen J, Vauvelle A, Hegselmann S, Denaxas S, Hemingway H, Langenberg C, Landmesser U, Deanfield J, Eils R. Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats. Nat Commun 2024; 15:4257. [PMID: 38763986 PMCID: PMC11102902 DOI: 10.1038/s41467-024-48568-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/03/2024] [Indexed: 05/21/2024] Open
Abstract
The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.
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Affiliation(s)
- Jakob Steinfeldt
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Benjamin Wild
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Thore Buergel
- Institute of Cardiovascular Sciences, University College London, London, UK
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Julius Upmeier Zu Belzen
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Andre Vauvelle
- Institute of Health Informatics, University College London, London, UK
| | - Stefan Hegselmann
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Massachusetts, USA
- Pattern Recognition and Image Analysis Lab, University of Münster, Münster, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Ulf Landmesser
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Berlin, Germany
| | - John Deanfield
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany.
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Kraljevic Z, Bean D, Shek A, Bendayan R, Hemingway H, Yeung JA, Deng A, Baston A, Ross J, Idowu E, Teo JT, Dobson RJB. Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. Lancet Digit Health 2024; 6:e281-e290. [PMID: 38519155 DOI: 10.1016/s2589-7500(24)00025-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). METHODS Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. FINDINGS Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91-100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. INTERPRETATION Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. FUNDING National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.
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Affiliation(s)
- Zeljko Kraljevic
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Dan Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Anthony Shek
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Harry Hemingway
- Health Data Research UK London and Institute of Health Informatics, University College London, London, UK; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Joshua Au Yeung
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Alfred Baston
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jack Ross
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Esther Idowu
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - James T Teo
- Department of Neurology, King's College Hospital National Health Service (NHS) Foundation Trust, London, UK; Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Health Data Research UK London and Institute of Health Informatics, University College London, London, UK; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK.
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3
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Hall M, Smith L, Wu J, Hayward C, Batty JA, Lambert PC, Hemingway H, Gale CP. Health outcomes after myocardial infarction: A population study of 56 million people in England. PLoS Med 2024; 21:e1004343. [PMID: 38358949 PMCID: PMC10868847 DOI: 10.1371/journal.pmed.1004343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. METHODS AND FINDINGS This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only-as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. CONCLUSIONS In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.
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Affiliation(s)
- Marlous Hall
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Lesley Smith
- Leeds Institute for Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Jianhua Wu
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
- Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
| | - Chris Hayward
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Jonathan A. Batty
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Paul C. Lambert
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom
- Charité Universitätsmedizin, Berlin, Germany
| | - Chris P. Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
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Katsoulis M, Lai AG, Kipourou DK, Gomes M, Banerjee A, Denaxas S, Lumbers RT, Tsilidis K, Kostara M, Belot A, Dale C, Sofat R, Leyrat C, Hemingway H, Diaz-Ordaz K. On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework. Int J Obes (Lond) 2023; 47:1309-1317. [PMID: 37884665 PMCID: PMC10663146 DOI: 10.1038/s41366-023-01396-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 09/17/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND/OBJECTIVES When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) 'when is time zero?' and (ii) 'which confounders should we account for?' From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results. METHODS We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required. Then we simulated data from an observational study where modelling is needed. We demonstrate the problems of each of these methods and suggest a strategy. INTERVENTIONS weight loss/gain vs maintenance. RESULTS The recommended method defines time-zero at enrolment, but adjustment for confounders (or exclusion of individuals based on levels of confounders) should be performed both at enrolment and the 1st follow-up. CONCLUSIONS The implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short.
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Affiliation(s)
- M Katsoulis
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK.
| | - A G Lai
- Institute of Health Informatics, University College London, London, UK
| | - D K Kipourou
- Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- AstraZeneca, London, UK
| | - M Gomes
- Department of Applied Health Research, University College London, London, UK
| | - A Banerjee
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
- Barts Health NHS Trust, The Royal London Hospital, London, UK
| | - S Denaxas
- Institute of Health Informatics, University College London, London, UK
- Alan Turing Institute, London, UK
| | - R T Lumbers
- Institute of Health Informatics, University College London, London, UK
| | - K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Maria Kostara
- Department of Pediatrics, University Hospital of Ioannina, Ioannina, Greece
| | - A Belot
- Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - C Dale
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - R Sofat
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - C Leyrat
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - H Hemingway
- Institute of Health Informatics, University College London, London, UK
| | - K Diaz-Ordaz
- Dept of Statistical Science, Faculty of Maths & Physical Sciences, University College London, London, UK
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Prugger C, Perier MC, Gonzalez-Izquierdo A, Hemingway H, Denaxas S, Empana JP. Incidence of 12 common cardiovascular diseases and subsequent mortality risk in the general population. Eur J Prev Cardiol 2023; 30:1715-1722. [PMID: 37294923 DOI: 10.1093/eurjpc/zwad192] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/25/2023] [Accepted: 06/03/2023] [Indexed: 06/11/2023]
Abstract
BACKGROUND Incident events of cardiovascular diseases (CVDs) are heterogenous and may result in different mortality risks. Such evidence may help inform patient and physician decisions in CVD prevention and risk factor management. AIMS This study aimed to determine the extent to which incident events of common CVD show heterogeneous associations with subsequent mortality risk in the general population. METHODS AND RESULTS Based on England-wide linked electronic health records, we established a cohort of 1 310 518 people ≥30 years of age initially free of CVD and followed up for non-fatal events of 12 common CVD and cause-specific mortality. The 12 CVDs were considered as time-varying exposures in Cox's proportional hazards models to estimate hazard rate ratios (HRRs) with 95% confidence intervals (CIs). Over the median follow-up of 4.2 years (2010-16), 81 516 non-fatal CVD, 10 906 cardiovascular deaths, and 40 843 non-cardiovascular deaths occurred. All 12 CVDs were associated with increased risk of cardiovascular mortality, with HRR (95% CI) ranging from 1.67 (1.47-1.89) for stable angina to 7.85 (6.62-9.31) for haemorrhagic stroke. All 12 CVDs were also associated with increased non-cardiovascular and all-cause mortality risk but to a lesser extent: HRR (95% CI) ranged from 1.10 (1.00-1.22) to 4.55 (4.03-5.13) and from 1.24 (1.13-1.35) to 4.92 (4.44-5.46) for transient ischaemic attack and sudden cardiac arrest, respectively. CONCLUSION Incident events of 12 common CVD show significant adverse and markedly differential associations with subsequent cardiovascular, non-cardiovascular, and all-cause mortality risk in the general population.
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Affiliation(s)
- Christof Prugger
- Institute of Public Health, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marie-Cécile Perier
- INSERM U970, Paris Cardiovascular Research Centre (PARCC), Integrative Epidemiology of Cardiovascular Diseases, Université Paris Cité, 56 rue Leblanc, 75015 Paris, France
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA London, UK
- Health Data Research UK, 215 Euston Road, NW1 2DA London, UK
- UCL Hospitals Biomedical Research Centers (BRC), 270 Tottenham Court Road, 215 Euston Road, NW1 2BE London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA London, UK
- Health Data Research UK, 215 Euston Road, NW1 2DA London, UK
- UCL Hospitals Biomedical Research Centers (BRC), 270 Tottenham Court Road, 215 Euston Road, NW1 2BE London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA London, UK
- Health Data Research UK, 215 Euston Road, NW1 2DA London, UK
- UCL Hospitals Biomedical Research Centers (BRC), 270 Tottenham Court Road, 215 Euston Road, NW1 2BE London, UK
- British Heart Foundation Data Science Center, 215 Euston Road, NW1 2BE London, UK
| | - Jean-Philippe Empana
- INSERM U970, Paris Cardiovascular Research Centre (PARCC), Integrative Epidemiology of Cardiovascular Diseases, Université Paris Cité, 56 rue Leblanc, 75015 Paris, France
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Hingorani AD, Gratton J, Finan C, Schmidt AF, Patel R, Sofat R, Kuan V, Langenberg C, Hemingway H, Morris JK, Wald NJ. Performance of polygenic risk scores in screening, prediction, and risk stratification: secondary analysis of data in the Polygenic Score Catalog. BMJ Med 2023; 2:e000554. [PMID: 37859783 PMCID: PMC10582890 DOI: 10.1136/bmjmed-2023-000554] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/31/2023] [Indexed: 10/21/2023]
Abstract
Objective To clarify the performance of polygenic risk scores in population screening, individual risk prediction, and population risk stratification. Design Secondary analysis of data in the Polygenic Score Catalog. Setting Polygenic Score Catalog, April 2022. Secondary analysis of 3915 performance metric estimates for 926 polygenic risk scores for 310 diseases to generate estimates of performance in population screening, individual risk, and population risk stratification. Participants Individuals contributing to the published studies in the Polygenic Score Catalog. Main outcome measures Detection rate for a 5% false positive rate (DR5) and the population odds of becoming affected given a positive result; individual odds of becoming affected for a person with a particular polygenic score; and odds of becoming affected for groups of individuals in different portions of a polygenic risk score distribution. Coronary artery disease and breast cancer were used as illustrative examples. Results For performance in population screening, median DR5 for all polygenic risk scores and all diseases studied was 11% (interquartile range 8-18%). Median DR5 was 12% (9-19%) for polygenic risk scores for coronary artery disease and 10% (9-12%) for breast cancer. The population odds of becoming affected given a positive results were 1:8 for coronary artery disease and 1:21 for breast cancer, with background 10 year odds of 1:19 and 1:41, respectively, which are typical for these diseases at age 50. For individual risk prediction, the corresponding 10 year odds of becoming affected for individuals aged 50 with a polygenic risk score at the 2.5th, 25th, 75th, and 97.5th centiles were 1:54, 1:29, 1:15, and 1:8 for coronary artery disease and 1:91, 1:56, 1:34, and 1:21 for breast cancer. In terms of population risk stratification, at age 50, the risk of coronary artery disease was divided into five groups, with 10 year odds of 1:41 and 1:11 for the lowest and highest quintile groups, respectively. The 10 year odds was 1:7 for the upper 2.5% of the polygenic risk score distribution for coronary artery disease, a group that contributed 7% of cases. The corresponding estimates for breast cancer were 1:72 and 1:26 for the lowest and highest quintile groups, and 1:19 for the upper 2.5% of the distribution, which contributed 6% of cases. Conclusion Polygenic risk scores performed poorly in population screening, individual risk prediction, and population risk stratification. Strong claims about the effect of polygenic risk scores on healthcare seem to be disproportionate to their performance.
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Affiliation(s)
- Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - Jasmine Gratton
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - A Floriaan Schmidt
- Institute of Cardiovascular Science, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
- University Medical Centre Utrecht, Utrecht, Netherlands
| | - Riyaz Patel
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - Reecha Sofat
- Health Data Research UK, London, UK
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Valerie Kuan
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charite Universitatzmedizin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Harry Hemingway
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Joan K Morris
- Population Health Research Institute, St George's University of London, London, UK
| | - Nicholas J Wald
- Institute of Health Informatics, University College London, London, UK
- Population Health Research Institute, St George's University of London, London, UK
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Hartmann S, Yasmeen S, Jacobs BM, Denaxas S, Pirmohamed M, Gamazon ER, Caulfield MJ, Hemingway H, Pietzner M, Langenberg C. ADRA2A and IRX1 are putative risk genes for Raynaud's phenomenon. Nat Commun 2023; 14:6156. [PMID: 37828025 PMCID: PMC10570309 DOI: 10.1038/s41467-023-41876-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023] Open
Abstract
Raynaud's phenomenon (RP) is a common vasospastic disorder that causes severe pain and ulcers, but despite its high reported heritability, no causal genes have been robustly identified. We conducted a genome-wide association study including 5,147 RP cases and 439,294 controls, based on diagnoses from electronic health records, and identified three unreported genomic regions associated with the risk of RP (p < 5 × 10-8). We prioritized ADRA2A (rs7090046, odds ratio (OR) per allele: 1.26; 95%-CI: 1.20-1.31; p < 9.6 × 10-27) and IRX1 (rs12653958, OR: 1.17; 95%-CI: 1.12-1.22, p < 4.8 × 10-13) as candidate causal genes through integration of gene expression in disease relevant tissues. We further identified a likely causal detrimental effect of low fasting glucose levels on RP risk (rG = -0.21; p-value = 2.3 × 10-3), and systematically highlighted drug repurposing opportunities, like the antidepressant mirtazapine. Our results provide the first robust evidence for a strong genetic contribution to RP and highlight a so far underrated role of α2A-adrenoreceptor signalling, encoded at ADRA2A, as a possible mechanism for hypersensitivity to catecholamine-induced vasospasms.
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Affiliation(s)
- Sylvia Hartmann
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Summaira Yasmeen
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Benjamin M Jacobs
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalised Medicine, University Liverpool, Liverpool, UK
| | - Eric R Gamazon
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Mark J Caulfield
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Jordan KP, Rathod-Mistry T, van der Windt DA, Bailey J, Chen Y, Clarson L, Denaxas S, Hayward RA, Hemingway H, Kyriacou T, Mamas MA. Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. Eur J Prev Cardiol 2023; 30:1151-1161. [PMID: 36895179 PMCID: PMC10442054 DOI: 10.1093/eurjpc/zwad055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/06/2022] [Accepted: 02/21/2023] [Indexed: 03/11/2023]
Abstract
AIMS Most adults presenting in primary care with chest pain symptoms will not receive a diagnosis ('unattributed' chest pain) but are at increased risk of cardiovascular events. To assess within patients with unattributed chest pain, risk factors for cardiovascular events and whether those at greatest risk of cardiovascular disease can be ascertained by an existing general population risk prediction model or by development of a new model. METHODS AND RESULTS The study used UK primary care electronic health records from the Clinical Practice Research Datalink linked to admitted hospitalizations. Study population was patients aged 18 plus with recorded unattributed chest pain 2002-2018. Cardiovascular risk prediction models were developed with external validation and comparison of performance to QRISK3, a general population risk prediction model. There were 374 917 patients with unattributed chest pain in the development data set. The strongest risk factors for cardiovascular disease included diabetes, atrial fibrillation, and hypertension. Risk was increased in males, patients of Asian ethnicity, those in more deprived areas, obese patients, and smokers. The final developed model had good predictive performance (external validation c-statistic 0.81, calibration slope 1.02). A model using a subset of key risk factors for cardiovascular disease gave nearly identical performance. QRISK3 underestimated cardiovascular risk. CONCLUSION Patients presenting with unattributed chest pain are at increased risk of cardiovascular events. It is feasible to accurately estimate individual risk using routinely recorded information in the primary care record, focusing on a small number of risk factors. Patients at highest risk could be targeted for preventative measures.
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Affiliation(s)
- Kelvin P Jordan
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
| | - Trishna Rathod-Mistry
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford OX3 7LD, UK
| | - Danielle A van der Windt
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
| | - James Bailey
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
| | - Ying Chen
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
- Wisdom Lake Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China
| | - Lorna Clarson
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, UK
- Health Data Research UK, University College London, 222 Euston Road, London NW1 2DA, UK
| | - Richard A Hayward
- School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, Maple House 1st floor, 149 Tottenham Court Road, London W1T 7DN, UK
| | - Theocharis Kyriacou
- School of Computing and Mathematics, Keele University, Staffordshire ST5 5AA, UK
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK
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9
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Banerjee A, Dashtban A, Chen S, Pasea L, Thygesen JH, Fatemifar G, Tyl B, Dyszynski T, Asselbergs FW, Lund LH, Lumbers T, Denaxas S, Hemingway H. Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study. Lancet Digit Health 2023; 5:e370-e379. [PMID: 37236697 DOI: 10.1016/s2589-7500(23)00065-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 03/01/2023] [Accepted: 03/16/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data. METHODS In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12). FINDINGS We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness. INTERPRETATION Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials. FUNDING European Union Innovative Medicines Initiative-2.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK; Barts Health NHS Trust, London, UK; Department of Cardiology, University College London Hospitals NHS Trust, London, UK; NIHR Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK.
| | - Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Suliang Chen
- Institute of Health Informatics, University College London, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | - Johan H Thygesen
- Institute of Health Informatics, University College London, London, UK
| | | | - Benoit Tyl
- Medical Affairs, Pharmaceuticals, Bayer HealthCare, Paris, France
| | | | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK; NIHR Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK; Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
| | - Lars H Lund
- Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; Heart and Vascular Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Tom Lumbers
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK; Barts Health NHS Trust, London, UK; Department of Cardiology, University College London Hospitals NHS Trust, London, UK; NIHR Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, London, UK; NIHR Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK
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Dashtban A, Mizani MA, Pasea L, Denaxas S, Corbett R, Mamza JB, Gao H, Morris T, Hemingway H, Banerjee A. Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals. EBioMedicine 2023; 89:104489. [PMID: 36857859 PMCID: PMC9989643 DOI: 10.1016/j.ebiom.2023.104489] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions. METHODS We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter). FINDINGS After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%). MEDICATIONS Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD. INTERPRETATION In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction. FUNDING AstraZeneca UK Ltd, Health Data Research UK.
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Affiliation(s)
- Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | | | - Jil B Mamza
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - He Gao
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; Barts Health NHS Trust, London, UK; University College London Hospitals NHS Trust, London, UK.
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11
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Mueller SH, Lai AG, Valkovskaya M, Michailidou K, Bolla MK, Wang Q, Dennis J, Lush M, Abu-Ful Z, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baert T, Freeman LEB, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Brenner H, Brucker SY, Buys SS, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Choi JY, Chung WK, Colonna SV, Cornelissen S, Couch FJ, Czene K, Daly MB, Devilee P, Dörk T, Dossus L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Engel C, Evans DG, Fasching PA, Fletcher O, Flyger H, Gago-Dominguez M, Gao YT, García-Closas M, García-Sáenz JA, Genkinger J, Gentry-Maharaj A, Grassmann F, Guénel P, Gündert M, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Harkness EF, Harrington PA, Hartikainen JM, Hartman M, Hein A, Ho WK, Hooning MJ, Hoppe R, Hopper JL, Houlston RS, Howell A, Hunter DJ, Huo D, Ito H, Iwasaki M, Jakubowska A, Janni W, John EM, Jones ME, Jung A, Kaaks R, Kang D, Khusnutdinova EK, Kim SW, Kitahara CM, Koutros S, Kraft P, Kristensen VN, Kubelka-Sabit K, Kurian AW, Kwong A, Lacey JV, Lambrechts D, Le Marchand L, Li J, Linet M, Lo WY, Long J, Lophatananon A, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Menon U, Muir K, Murphy RA, Nevanlinna H, Newman WG, Niederacher D, O'Brien KM, Obi N, Offit K, Olopade OI, Olshan AF, Olsson H, Park SK, Patel AV, Patel A, Perou CM, Peto J, Pharoah PDP, Plaseska-Karanfilska D, Presneau N, Rack B, Radice P, Ramachandran D, Rashid MU, Rennert G, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt MK, Schmutzler RK, Schneider MO, Scott C, Shah M, Sharma P, Shen CY, Shu XO, Simard J, Surowy H, Tamimi RM, Tapper WJ, Taylor JA, Teo SH, Teras LR, Toland AE, Tollenaar RAEM, Torres D, Torres-Mejía G, Troester MA, Truong T, Vachon CM, Vijai J, Weinberg CR, Wendt C, Winqvist R, Wolk A, Wu AH, Yamaji T, Yang XR, Yu JC, Zheng W, Ziogas A, Ziv E, Dunning AM, Easton DF, Hemingway H, Hamann U, Kuchenbaecker KB. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry. Genome Med 2023; 15:7. [PMID: 36703164 PMCID: PMC9878779 DOI: 10.1186/s13073-022-01152-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/16/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. METHODS We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. RESULTS In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. CONCLUSIONS Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.
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Affiliation(s)
| | - Alvina G Lai
- Institute of Health Informatics, University College London, London, UK
| | | | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, 2371, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2371, Nicosia, Cyprus
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Zomoruda Abu-Ful
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, 35254, Haifa, Israel
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, 92617, USA
| | - Natalia N Antonenkova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, 223040, Minsk, Belarus
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Annelie Augustinsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, 222 42, Lund, Sweden
| | - Thais Baert
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, 3000, Louvain, Belgium
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Javier Benitez
- Biomedical Network On Rare Diseases (CIBERER), 28029, Madrid, Spain
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Madrid, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, 450054, Russia
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, 00290, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, 70185, Örebro, Sweden
| | - Natalia V Bogdanova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, 223040, Minsk, Belarus
- Department of Radiation Oncology, Hannover Medical School, 30625, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, 30625, Hannover, Germany
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, 2730, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, 2730, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, 20141, Milan, Italy
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Sara Y Brucker
- Department of Gynecology and Obstetrics, University of Tübingen, 72076, Tübingen, Germany
| | - Saundra S Buys
- Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, 84112, USA
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, 36312, Vigo, Spain
| | - Tsun L Chan
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
- Department of Molecular Pathology, Hong Kong Sanatorium and Hospital, Hong Kong, China
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, 03080, Korea
- Cancer Research Institute, Seoul National University, Seoul, 03080, Korea
- Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, 03080, Korea
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, 10032, USA
| | - Sarah V Colonna
- Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, 84112, USA
| | - Sten Cornelissen
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, 1066 CX, The Netherlands
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, 19111, USA
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, 30625, Hannover, Germany
| | - Laure Dossus
- Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), 69372, Lyon, France
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London, W1W 6UW, UK
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Arif B Ekici
- Institute of Human Genetics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107, Leipzig, Germany
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, 04103, Leipzig, Germany
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, 90095, USA
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, 2730, Herlev, Denmark
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, International Cancer Genetics and Epidemiology Group, Fundación Pœblica Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, 15706, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92037, USA
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, 20032, China
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - José A García-Sáenz
- Medical Oncology Department, Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), 28040, Madrid, Spain
| | - Jeanine Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | | | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden
- Health and Medical University, 14471, Potsdam, Germany
| | - Pascal Guénel
- Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, INSERM, University Paris-Saclay, 94805, Villejuif, France
| | - Melanie Gündert
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), C08069120, Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, 69120, Heidelberg, Germany
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65, Stockholm, Sweden
- Department of Oncology, 118 83, Sšdersjukhuset, Stockholm, Sweden
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PT, UK
- Nightingale and Genesis Prevention Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M23 9LT, UK
- NIHR Manchester Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| | - Patricia A Harrington
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Jaana M Hartikainen
- Translational Cancer Research Area, University of Eastern Finland, 70210, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, 70210, Kuopio, Finland
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, 119077, Singapore
- Department of Surgery, National University Health System, Singapore, 119228, Singapore
| | - Alexander Hein
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | - Weang-Kee Ho
- Department of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, 43500, Semenyih, Selangor, Malaysia
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, 47500, Selangor, Malaysia
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, 3015 GD, The Netherlands
| | - Reiner Hoppe
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376, Stuttgart, Germany
- University of Tübingen, 72074, Tübingen, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, M13 9PL, UK
| | - David J Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Dezheng Huo
- Center for Clinical Cancer Genetics, The University of Chicago, Chicago, IL, 60637, USA
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya, 464-8681, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, Center for Public Health Sciences, National Cancer Center Institute for Cancer Control, Tokyo, 104-0045, Japan
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, 71-252, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, 71-252, Szczecin, Poland
| | - Wolfgang Janni
- Department of Gynaecology and Obstetrics, University Hospital Ulm, 89075, Ulm, Germany
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Daehee Kang
- Cancer Research Institute, Seoul National University, Seoul, 03080, Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Elza K Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, 450054, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, 450000, Russia
| | - Sung-Won Kim
- Department of Surgery, Daerim Saint Mary's Hospital, Seoul, 07442, Korea
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Vessela N Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0450, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, 0379, Oslo, Norway
| | - Katerina Kubelka-Sabit
- Department of Histopathology and Cytology, Clinical Hospital Acibadem Sistina, Skopje, 1000, Republic of North Macedonia
| | - Allison W Kurian
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
- Department of Surgery, The University of Hong Kong, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong, China
| | - James V Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, 91010, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, 91010, USA
| | - Diether Lambrechts
- VIB Center for Cancer Biology, 3001, Louvain, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, 3000, Louvain, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Jingmei Li
- Human Genetics Division, Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Martha Linet
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Wing-Yee Lo
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376, Stuttgart, Germany
- University of Tübingen, 72074, Tübingen, Germany
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, 70210, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, 70210, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Sara Margolin
- Department of Oncology, 118 83, Sšdersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Sšdersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden
| | - Keitaro Matsuo
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, 464-8681, Japan
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, 711 10, Heraklion, Greece
| | - Usha Menon
- Institute of Clinical Trials and Methodology, University College London, London, WC1V 6LJ, UK
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
| | - Rachel A Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Cancer Control Research, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, 00290, Helsinki, Finland
| | - William G Newman
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| | - Dieter Niederacher
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, 27709, USA
| | - Nadia Obi
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, 222 42, Lund, Sweden
| | - Sue K Park
- Cancer Research Institute, Seoul National University, Seoul, 03080, Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Alpa V Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, 30303, USA
| | - Achal Patel
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles M Perou
- Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA, Skopje, 1000, Republic of North Macedonia
| | - Nadege Presneau
- School of Life Sciences, University of Westminster, London, W1W 6UW, UK
| | - Brigitte Rack
- Department of Gynaecology and Obstetrics, University Hospital Ulm, 89075, Ulm, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale Dei Tumori (INT), 20133, Milan, Italy
| | - Dhanya Ramachandran
- Gynaecology Research Unit, Hannover Medical School, 30625, Hannover, Germany
| | - Muhammad U Rashid
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, 54000, Pakistan
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, 35254, Haifa, Israel
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, 28222, Madrid, Spain
| | - Kathryn J Ruddy
- Department of Oncology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Matthias Ruebner
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | | | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, 27709, USA
| | - Elinor J Sawyer
- School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London, SE1 9RT, UK
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, 1066 CX, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, 1066 CX, The Netherlands
| | - Rita K Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931, Cologne, Germany
| | - Michael O Schneider
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054, Erlangen, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, 66205, USA
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
- School of Public Health, China Medical University, Taichung, Taiwan
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Québec City, QC, G1V 4G2, Canada
| | - Harald Surowy
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), C08069120, Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, 69120, Heidelberg, Germany
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 10065, USA
| | - William J Tapper
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, 27709, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, 27709, USA
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, 47500, Selangor, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lauren R Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, 30303, USA
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, 43210, USA
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands
| | - Diana Torres
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Institute of Human Genetics, Pontificia Universidad Javeriana, 110231, Bogota, Colombia
| | - Gabriela Torres-Mejía
- Center for Population Health Research, National Institute of Public Health, 62100, Cuernavaca, Morelos, Mexico
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, INSERM, University Paris-Saclay, 94805, Villejuif, France
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Joseph Vijai
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, 27709, USA
| | - Camilla Wendt
- Department of Clinical Science and Education, Sšdersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, 90570, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, 90570, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 751 05, Uppsala, Sweden
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Taiki Yamaji
- Division of Epidemiology, Center for Public Health Sciences, National Cancer Center Institute for Cancer Control, Tokyo, 104-0045, Japan
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA
| | - Jyh-Cherng Yu
- Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Argyrios Ziogas
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, 92617, USA
| | - Elad Ziv
- Department of Medicine, Diller Family Comprehensive Cancer Center, Institute for Human Genetics, UCSF Helen, University of California San Francisco, San Francisco, CA, 94115, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
- The Alan Turing Institute, London, UK
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Karoline B Kuchenbaecker
- Division of Psychiatry, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
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12
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Kuan V, Denaxas S, Patalay P, Nitsch D, Mathur R, Gonzalez-Izquierdo A, Sofat R, Partridge L, Roberts A, Wong ICK, Hingorani M, Chaturvedi N, Hemingway H, Hingorani AD. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit Health 2023; 5:e16-e27. [PMID: 36460578 DOI: 10.1016/s2589-7500(22)00187-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population. METHODS In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age. FINDINGS We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50·5%) were women and girls, 1 916 751 (49·5%) were men and boys, 2 666 234 (68·9%) were White, 155 435 (4·0%) were south Asian, and 98 815 (2·6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47·8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40·3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80·5%] of 1 690 521 vs 1 161 308 [70·8%] of 1 639 593). White individuals (2 097 536 [78·7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60·1%] of 98 815) or south Asian individuals (93 617 [60·2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups. INTERPRETATION Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials. FUNDING UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.
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Affiliation(s)
- Valerie Kuan
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; UCL BHF Research Accelerator, University College London, London, UK; Alan Turing Institute, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK; British Heart Foundation Data Science Centre, HDR UK, London, UK
| | - Praveetha Patalay
- Centre for Longitudinal Studies, University College London, London, UK; MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Rohini Mathur
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Centre for Primary Care, Wolfson Institute of Primary Care, Queen Mary University of London, London, UK
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Reecha Sofat
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK; British Heart Foundation Data Science Centre, HDR UK, London, UK
| | - Linda Partridge
- Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, University College London, London, UK; Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Amanda Roberts
- Nottingham Support Group for Carers of Children with Eczema, Nottingham, UK
| | - Ian C K Wong
- School of Pharmacy, University College London, London, UK; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China; Aston Pharmacy School, Aston University, Birmingham, UK
| | | | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK
| | - Aroon D Hingorani
- UCL BHF Research Accelerator, University College London, London, UK; Institute of Cardiovascular Science, University College London, London, UK; University College London Hospitals NIHR Biomedical Research Centre, London, UK
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13
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Mizani MA, Dashtban A, Pasea L, Lai AG, Thygesen J, Tomlinson C, Handy A, Mamza JB, Morris T, Khalid S, Zaccardi F, Macleod MJ, Torabi F, Canoy D, Akbari A, Berry C, Bolton T, Nolan J, Khunti K, Denaxas S, Hemingway H, Sudlow C, Banerjee A. Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study. J R Soc Med 2023; 116:10-20. [PMID: 36374585 PMCID: PMC9909113 DOI: 10.1177/01410768221131897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/24/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. DESIGN An EHR-based, retrospective cohort study. SETTING Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). PARTICIPANTS In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. MAIN OUTCOME MEASURES One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. RESULTS From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31-4.38) and IR was 6.27% (95% CI, 6.26-6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. CONCLUSIONS We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions.
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Affiliation(s)
- Mehrdad A Mizani
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
- BHF Data Science Centre, Health Data Research UK, London, NW1
2BE, UK
| | - Ashkan Dashtban
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Alvina G Lai
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Johan Thygesen
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Chris Tomlinson
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Alex Handy
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Jil B Mamza
- Medical and Scientific Affairs, BioPharmaceuticals Medical,
AstraZeneca, Cambridge, CB2 0AA, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical,
AstraZeneca, Cambridge, CB2 0AA, UK
| | - Sara Khalid
- Nuffield Department of Orthopaedics, Rheumatology and
Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7HE, UK
| | - Francesco Zaccardi
- Leicester Diabetes Centre, University of Leicester, Leicester,
LE5 4PW, UK
| | - Mary Joan Macleod
- School of Medicine, Medical Sciences and Nutrition, University
of Aberdeen, Aberdeen, AB24 3FX, UK
| | - Fatemeh Torabi
- Faculty of Medicine, Health and Life Science, Swansea
University, Swansea, SA2 8QA, UK
| | - Dexter Canoy
- Nuffield Department of Women’s and Reproductive Health,
University of Oxford, Oxford, OX3 9DU, UK
| | - Ashley Akbari
- Faculty of Medicine, Health and Life Science, Swansea
University, Swansea, SA2 8QA, UK
| | - Colin Berry
- Institute of Cardiovascular and Medical Sciences, University of
Glasgow, Glasgow, G12 8TA, UK
| | - Thomas Bolton
- BHF Data Science Centre, Health Data Research UK, London, NW1
2BE, UK
| | - John Nolan
- BHF Data Science Centre, Health Data Research UK, London, NW1
2BE, UK
| | - Kamlesh Khunti
- Leicester Diabetes Centre, University of Leicester, Leicester,
LE5 4PW, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - Cathie Sudlow
- BHF Data Science Centre, Health Data Research UK, London, NW1
2BE, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
| | - on behalf of the CVD-COVID-UK Consortium
- Institute of Health Informatics, University College London,
London NW1 2DA, UK
- BHF Data Science Centre, Health Data Research UK, London, NW1
2BE, UK
- Medical and Scientific Affairs, BioPharmaceuticals Medical,
AstraZeneca, Cambridge, CB2 0AA, UK
- Nuffield Department of Orthopaedics, Rheumatology and
Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7HE, UK
- Leicester Diabetes Centre, University of Leicester, Leicester,
LE5 4PW, UK
- School of Medicine, Medical Sciences and Nutrition, University
of Aberdeen, Aberdeen, AB24 3FX, UK
- Faculty of Medicine, Health and Life Science, Swansea
University, Swansea, SA2 8QA, UK
- Nuffield Department of Women’s and Reproductive Health,
University of Oxford, Oxford, OX3 9DU, UK
- Institute of Cardiovascular and Medical Sciences, University of
Glasgow, Glasgow, G12 8TA, UK
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14
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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15
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Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Banerjee A, Beger B, Brobert G, Casadei B, Ceccarelli C, Cowie MR, Crea F, Cronin M, Denaxas S, Derix A, Fitzsimons D, Fredriksson M, Gale CP, Gkoutos GV, Goettsch W, Hemingway H, Ingvar M, Jonas A, Kazmierski R, Løgstrup S, Thomas Lumbers R, Lüscher TF, McGreavy P, Piña IL, Roessig L, Steinbeisser C, Sundgren M, Tyl B, van Thiel G, van Bochove K, Vardas PE, Villanueva T, Vrana M, Weber W, Weidinger F, Windecker S, Wood A, Grobbee DE. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research. Eur Heart J 2022; 43:3578-3588. [PMID: 36208161 PMCID: PMC9452067 DOI: 10.1093/eurheartj/ehac426] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/29/2022] Open
Abstract
Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.
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Affiliation(s)
- Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Stephan Achenbach
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan D Anker
- Department of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Germany
| | - Dan Atar
- Department of Cardiology, Oslo University Hospital, Ulleval, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Colin Baigent
- MRC Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Amitava Banerjee
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | | | | | - Barbara Casadei
- Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford NIHR Oxford Biomedical Research Centre, Oxford, UK
| | | | - Martin R Cowie
- Royal Brompton Hospital, Division of Guy’s St Thomas’ NHS Foundation Trust, London, UK
- School of Cardiovascular Medicine Sciences, King’s College London, London, UK
| | - Filippo Crea
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
- European Heart Journal, Oxford University Press, University of Oxford, Oxford, UK
| | - Maureen Cronin
- Vifor Pharma, Glattbrugg, Switzerland and Ava AG, Zurich, Switzerland
| | - Spiros Denaxas
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Alan Turing Institute, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | | | - Donna Fitzsimons
- School of Nursing and Midwifery, Queen’s University Belfast, Northern Ireland
| | - Martin Fredriksson
- Late Clinical Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Chris P Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Georgios V Gkoutos
- University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Wim Goettsch
- National Health Care Institute (ZIN), Diemen, Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Harry Hemingway
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Martin Ingvar
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
- Department of Neuroradiology, Karolinska University Hospital Stockholm, Stockholm, Sweden
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | - Robert Kazmierski
- Office of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - R Thomas Lumbers
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Barts Health NHS Trust and University College London Hospitals NHS Trust
| | - Thomas F Lüscher
- Centre for Molecular Cardiology, University of Zurich, Zurich, Switzerland
- Research, Education & Development, Royal Brompton and Harefield Hospitals, London, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Paul McGreavy
- European Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium
| | - Ileana L Piña
- Central Michigan University College of Medicine, Midlands, MI, USA
- Centre for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Carl Steinbeisser
- Bayer AG, Leverkusen, Germany
- Steinbeisser Project Management, Munich, Germany
| | - Mats Sundgren
- Data Science AI, Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Benoît Tyl
- Centre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France
| | - Ghislaine van Thiel
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Panos E Vardas
- Hygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece
- European Heart Agency, European Society of Cardiology, Brussels, Belgium
| | | | | | | | | | - Stephan Windecker
- Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Angela Wood
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Diederick E Grobbee
- Department of Epidemiology, University Medical Centre Utrecht, Division Julius Centrum, Utrecht, Netherlands
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16
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Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Banerjee A, Beger B, Brobert G, Casadei B, Ceccarelli C, Cowie MR, Crea F, Cronin M, Denaxas S, Derix A, Fitzsimons D, Fredriksson M, Gale CP, Gkoutos GV, Goettsch W, Hemingway H, Ingvar M, Jonas A, Kazmierski R, Løgstrup S, Lumbers RT, Lüscher TF, McGreavy P, Piña IL, Roessig L, Steinbeisser C, Sundgren M, Tyl B, Thiel GV, Bochove KV, Vardas PE, Villanueva T, Vrana M, Weber W, Weidinger F, Windecker S, Wood A, Grobbee DE. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health 2022; 4:e757-e764. [PMID: 36050271 DOI: 10.1016/s2589-7500(22)00151-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022]
Abstract
Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.
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Affiliation(s)
- Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Department of Cardiology, Division of Heart and Lungs, University of Utrecht, Utrecht, Netherlands.
| | - Folkert W Asselbergs
- Health Data Research UK London, London, UK; Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Stefan D Anker
- Department of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research, Charité Universitätsmedizin, Berlin, Germany
| | - Dan Atar
- Department of Cardiology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Colin Baigent
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK; Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Amitava Banerjee
- Health Data Research UK London, London, UK; University College London Hospitals NHS Trust, London, UK
| | | | | | - Barbara Casadei
- Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford, UK
| | | | - Martin R Cowie
- Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK; School of Cardiovascular Medicine Sciences, King's College London, London, UK
| | - Filippo Crea
- European Heart Journal, Oxford University Press, University of Oxford, Oxford, UK; Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
| | - Maureen Cronin
- Vifor Pharma, Glattbrugg, Switzerland; Ava, Zurich, Switzerland
| | - Spiros Denaxas
- Health Data Research UK London, London, UK; Alan Turing Institute, London, UK; British Heart Foundation Data Science Centre, London, UK
| | | | - Donna Fitzsimons
- School of Nursing and Midwifery, Queen's University Belfast, Northern Ireland
| | - Martin Fredriksson
- Late Clinical Development, Cardiovascular, Renal and Metabolism, Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Chris P Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Wim Goettsch
- University Medical Centre Utrecht, and Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, Netherlands; National Health Care Institute, Diemen, Netherlands
| | | | - Martin Ingvar
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; Department of Neuroradiology, Karolinska University Hospital Stockholm, Stockholm, Sweden
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | - Robert Kazmierski
- Office of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - R Thomas Lumbers
- Health Data Research UK London, London, UK; Institute of Health Informatics, Barts Health NHS Trust and University College London Hospitals NHS Trust, London, UK
| | - Thomas F Lüscher
- Centre for Molecular Cardiology, University of Zurich, Zurich, Switzerland; Research, Education and Development, Royal Brompton and Harefield Hospitals, London, UK; Faculty of Medicine, Imperial College London, London, UK
| | - Paul McGreavy
- European Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium
| | - Ileana L Piña
- Centre for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA; College of Medicine, Central Michigan University, Midlands MI, USA
| | | | - Carl Steinbeisser
- Bayer, Leverkusen, Germany; Steinbeisser Project Management, Munich, Germany
| | - Mats Sundgren
- Data Science and Artificial Intelligence, Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Benoît Tyl
- Centre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France
| | - Ghislaine van Thiel
- Julius Center for Health Sciences and Primary Care, University of Utrecht, Utrecht, Netherlands
| | | | - Panos E Vardas
- Hygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece; European Heart Agency, European Society of Cardiology, Brussels, Belgium
| | | | | | - Wim Weber
- The British Medical Journal, London, UK
| | | | - Stephan Windecker
- Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Angela Wood
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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17
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Benedetto U, Sinha S, Mulla A, Glampson B, Davies J, Panoulas V, Gautama S, Papadimitriou D, Woods K, Elliott P, Hemingway H, Williams B, Asselbergs FW, Melikian N, Krasopoulos G, Sayeed R, Wendler O, Baig K, Chukwuemeka A, Angelini GD, Sterne JAC, Johnson T, Shah AM, Perera D, Patel RS, Kharbanda R, Channon KM, Mayet J, Kaura A. Implications of elevated troponin on time-to-surgery in non-ST elevation myocardial infarction (NIHR Health Informatics Collaborative: TROP-CABG study). Int J Cardiol 2022; 362:14-19. [PMID: 35487318 DOI: 10.1016/j.ijcard.2022.04.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/18/2022] [Accepted: 04/25/2022] [Indexed: 11/05/2022]
Abstract
Implications of elevated troponin on time-to-surgery in non-ST elevation myocardial infarction(NIHR Health Informatics Collaborative:TROP-CABG study). Benedetto et al. BACKGROUND: The optimal timing of coronary artery bypass grafting (CABG) in patients with non-ST elevation myocardial infarction (NSTEMI) and the utility of pre-operative troponin levels in decision-making remains unclear. We investigated (a) the association between peak pre-operative troponin and survival post-CABG in a large cohort of NSTEMI patients and (b) the interaction between troponin and time-to-surgery. METHODS AND RESULTS: Our cohort consisted of 1746 patients (1684 NSTEMI; 62 unstable angina) (mean age 69 ± 11 years,21% female) with recorded troponins that had CABG at five United Kingdom centers between 2010 and 2017. Time-segmented Cox regression was used to investigate the interaction of peak troponin and time-to-surgery on early (within 30 days) and late (beyond 30 days) survival. Average interval from peak troponin to surgery was 9 ± 15 days, with 1466 (84.0%) patients having CABG during the same admission. Sixty patients died within 30-days and another 211 died after a mean follow-up of 4 ± 2 years (30-day survival 0.97 ± 0.004 and 5-year survival 0.83 ± 0.01). Peak troponin was a strong predictor of early survival (adjusted P = 0.002) with a significant interaction with time-to-surgery (P interaction = 0.007). For peak troponin levels <100 times the upper limit of normal, there was no improvement in early survival with longer time-to-surgery. However, in patients with higher troponins, early survival increased progressively with a longer time-to-surgery, till day 10. Peak troponin did not influence survival beyond 30 days (adjusted P = 0.64). CONCLUSIONS: Peak troponin in NSTEMI patients undergoing CABG was a significant predictor of early mortality, strongly influenced the time-to-surgery and may prove to be a clinically useful biomarker in the management of these patients.
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Affiliation(s)
- Umberto Benedetto
- NIHR Bristol Biomedical Research Centre, University of Bristol and University Hospitals Bristol NHS Foundation Trust, Bristol, UK; University Gabriele D'Annunzio Chieti Pescara, Italy
| | - Shubhra Sinha
- NIHR Bristol Biomedical Research Centre, University of Bristol and University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Abdulrahim Mulla
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Benjamin Glampson
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Jim Davies
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Vasileios Panoulas
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Sanjay Gautama
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Dimitri Papadimitriou
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Kerrie Woods
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Paul Elliott
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK; Health Data Research UK, London, UK
| | - Harry Hemingway
- Health Data Research UK, London, UK; NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, UK
| | - Bryan Williams
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, UK
| | - Folkert W Asselbergs
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, UK
| | - Narbeh Melikian
- NIHR Guy's and St Thomas' Biomedical Research Centre, King's College London and King's College Hospital NHS Foundation Trust, London, UK
| | - George Krasopoulos
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Rana Sayeed
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Olaf Wendler
- NIHR Guy's and St Thomas' Biomedical Research Centre, King's College London and King's College Hospital NHS Foundation Trust, London, UK
| | - Kamran Baig
- NIHR Guy's and St Thomas' Biomedical Research Centre, King's College London and Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Andrew Chukwuemeka
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Gianni D Angelini
- NIHR Bristol Biomedical Research Centre, University of Bristol and University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
| | - Jonathan A C Sterne
- NIHR Bristol Biomedical Research Centre, University of Bristol and University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Tom Johnson
- NIHR Bristol Biomedical Research Centre, University of Bristol and University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Ajay M Shah
- NIHR Guy's and St Thomas' Biomedical Research Centre, King's College London and King's College Hospital NHS Foundation Trust, London, UK
| | - Divaka Perera
- NIHR Guy's and St Thomas' Biomedical Research Centre, King's College London and Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Riyaz S Patel
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, UK
| | - Rajesh Kharbanda
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Keith M Channon
- NIHR Oxford Biomedical Research Centre, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jamil Mayet
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
| | - Amit Kaura
- NIHR Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare NHS Trust, Hammersmith Hospital, London W12 0HS, UK
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18
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Dashtban A, Mizani MA, Denaxas S, Nitsch D, Quint J, Corbett R, Mamza JB, Morris T, Mamas M, Lawlor DA, Khunti K, Sudlow C, Hemingway H, Banerjee A. A retrospective cohort study predicting and validating impact of the COVID-19 pandemic in individuals with chronic kidney disease. Kidney Int 2022; 102:652-660. [PMID: 35724769 PMCID: PMC9212366 DOI: 10.1016/j.kint.2022.05.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/26/2022] [Accepted: 05/09/2022] [Indexed: 02/07/2023]
Abstract
Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment [NHSD TRE]) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink [CPRD]) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition[s] in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure (1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic.
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Affiliation(s)
- Ashkan Dashtban
- Institute of Health Informatics, University College London, London, UK
| | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Jennifer Quint
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, UK
| | - Richard Corbett
- Department of Nephrology, Imperial College Healthcare NHS Trust, London, UK
| | - Jil B Mamza
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Mamas Mamas
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, Keele, UK
| | - Deborah A Lawlor
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; Department of Cardiology, Barts Health NHS Trust, London, UK; Department of Cardiology, University College London Hospitals NHS Trust, London, UK.
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19
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Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Banerjee A, Beger B, Brobert G, Casadei B, Ceccarelli C, Cowie MR, Crea F, Cronin M, Denaxas S, Derix A, Fitzsimons D, Fredriksson M, Gale CP, Gkoutos GV, Goettsch W, Hemingway H, Ingvar M, Jonas A, Kazmierski R, Løgstrup S, Lumbers RT, Lüscher TF, McGreavy P, Piña IL, Roessig L, Steinbeisser C, Sundgren M, Tyl B, van Thiel G, van Bochove K, Vardas PE, Villanueva T, Vrana M, Weber W, Weidinger F, Windecker S, Wood A, Grobbee DE. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research. BMJ 2022; 378:e069048. [PMID: 36562446 PMCID: PMC9403753 DOI: 10.1136/bmj-2021-069048] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 12/27/2022]
Affiliation(s)
- Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Birmingham, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Stephan Achenbach
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan D Anker
- Department of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Germany
| | - Dan Atar
- Department of Cardiology, Oslo University Hospital, Ulleval, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Colin Baigent
- MRC Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Amitava Banerjee
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | | | | | - Barbara Casadei
- Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford NIHR Oxford Biomedical Research Centre, Oxford, UK
| | | | - Martin R Cowie
- Royal Brompton Hospital, Division of Guy's St Thomas' NHS Foundation Trust, London, UK
- School of Cardiovascular Medicine Sciences, King's College London, London, UK
| | - Filippo Crea
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
- European Heart Journal, Oxford University Press, University of Oxford, Oxford, UK
| | - Maureen Cronin
- Vifor Pharma, Glattbrugg, Switzerland and Ava AG, Zurich, Switzerland
| | - Spiros Denaxas
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Alan Turing Institute, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | | | - Donna Fitzsimons
- School of Nursing and Midwifery, Queen's University Belfast, Northern Ireland
| | - Martin Fredriksson
- Late Clinical Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Chris P Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Georgios V Gkoutos
- University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Wim Goettsch
- National Health Care Institute (ZIN), Diemen, Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Harry Hemingway
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Martin Ingvar
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
- Department of Neuroradiology, Karolinska University Hospital Stockholm, Stockholm, Sweden
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | - Robert Kazmierski
- Office of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - R Thomas Lumbers
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Barts Health NHS Trust and University College London Hospitals NHS Trust
| | - Thomas F Lüscher
- Centre for Molecular Cardiology, University of Zurich, Zurich, Switzerland
- Faculty of Medicine, Imperial College London, London, UK
| | - Paul McGreavy
- European Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium
| | - Ileana L Piña
- Central Michigan University College of Medicine, Midlands, MI, USA
- Centre for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Carl Steinbeisser
- Bayer AG, Leverkusen, Germany
- Steinbeisser Project Management, Munich, Germany
| | - Mats Sundgren
- Data Science AI, Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Benoît Tyl
- Centre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France
| | - Ghislaine van Thiel
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Panos E Vardas
- Hygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece
- European Heart Agency, European Society of Cardiology, Brussels, Belgium
| | | | | | | | | | - Stephan Windecker
- Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Angela Wood
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Diederick E Grobbee
- Department of Epidemiology, University Medical Centre Utrecht, Division Julius Centrum, Utrecht, Netherlands
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20
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Huang QQ, Sallah N, Dunca D, Trivedi B, Hunt KA, Hodgson S, Lambert SA, Arciero E, Wright J, Griffiths C, Trembath RC, Hemingway H, Inouye M, Finer S, van Heel DA, Lumbers RT, Martin HC, Kuchenbaecker K. Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals. Nat Commun 2022; 13:4664. [PMID: 35945198 PMCID: PMC9363492 DOI: 10.1038/s41467-022-32095-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/15/2022] [Indexed: 12/30/2022] Open
Abstract
Individuals with South Asian ancestry have a higher risk of heart disease than other groups but have been largely excluded from genetic research. Using data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort, we conducted genome-wide association studies of coronary artery disease and its key risk factors. Using power-adjusted transferability ratios, we found evidence for transferability for the majority of cardiometabolic loci powered to replicate. The performance of polygenic scores was high for lipids and blood pressure, but lower for BMI and coronary artery disease. Adding a polygenic score for coronary artery disease to clinical risk factors showed significant improvement in reclassification. In Mendelian randomisation using transferable loci as instruments, our findings were consistent with results in European-ancestry individuals. Taken together, trait-specific transferability of trait loci between populations is an important consideration with implications for risk prediction and causal inference.
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Affiliation(s)
- Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Neneh Sallah
- Institute of Health Informatics, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Diana Dunca
- Institute of Health Informatics, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Bhavi Trivedi
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Karen A Hunt
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sam Hodgson
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Elena Arciero
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service (NHS) Foundation Trust, Bradford, UK
| | - Chris Griffiths
- Institute of Population Health Sciences, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Richard C Trembath
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Research Excellence, Department of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Sarah Finer
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Hilary C Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Karoline Kuchenbaecker
- UCL Genetics Institute, University College London, London, UK.
- Division of Psychiatry, University College London, London, UK.
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21
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Thygesen JH, Tomlinson C, Hollings S, Mizani MA, Handy A, Akbari A, Banerjee A, Cooper J, Lai AG, Li K, Mateen BA, Sattar N, Sofat R, Torralbo A, Wu H, Wood A, Sterne JAC, Pagel C, Whiteley WN, Sudlow C, Hemingway H, Denaxas S. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Lancet Digit Health 2022; 4:e542-e557. [PMID: 35690576 PMCID: PMC9179175 DOI: 10.1016/s2589-7500(22)00091-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/15/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING British Heart Foundation Data Science Centre, led by Health Data Research UK.
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Affiliation(s)
- Johan H Thygesen
- Institute of Health Informatics, University College London, London, UK
| | - Christopher Tomlinson
- Institute of Health Informatics, University College London, London, UK; UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | | | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK
| | - Alex Handy
- Institute of Health Informatics, University College London, London, UK
| | - Ashley Akbari
- Population Data Science, Swansea University, Swansea, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
| | - Jennifer Cooper
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alvina G Lai
- Institute of Health Informatics, University College London, London, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK; UK Research and Innovation Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK; The Wellcome Trust, London, UK
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Ana Torralbo
- Institute of Health Informatics, University College London, London, UK
| | - Honghan Wu
- Institute of Health Informatics, University College London, London, UK
| | - Angela Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
| | - Jonathan A C Sterne
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christina Pagel
- Clinical Operational Research Unit, University College London, London, UK
| | - William N Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cathie Sudlow
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Health Data Research UK, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London, UK; Health Data Research UK, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; British Heart Foundation Research Accelerator, University College London, London, UK; University College London Hospitals Biomedical Research Centre, University College London, London, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK; Health Data Research UK, London, UK.
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22
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Patel RS, Pasea L, Soran H, Downie P, Jones R, Hingorani AD, Neely D, Denaxas S, Hemingway H. Elevated plasma triglyceride concentration and risk of adverse clinical outcomes in 1.5 million people: a CALIBER linked electronic health record study. Cardiovasc Diabetol 2022; 21:102. [PMID: 35681241 PMCID: PMC9185961 DOI: 10.1186/s12933-022-01525-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Assessing the spectrum of disease risk associated with hypertriglyceridemia is needed to inform potential benefits from emerging triglyceride lowering treatments. We sought to examine the associations between a full range of plasma triglyceride concentration with five clinical outcomes. METHODS We used linked data from primary and secondary care for 15 M people, to explore the association between triglyceride concentration and risk of acute pancreatitis, chronic pancreatitis, new onset diabetes, myocardial infarction and all-cause mortality, over a median of 6-7 years follow up. RESULTS Triglyceride concentration was available for 1,530,411 individuals (mean age 56·6 ± 15·6 years, 51·4% female), with a median of 1·3 mmol/L (IQR: 0.9.to 1.9). Severe hypertriglyceridemia, defined as > 10 mmol/L, was identified in 3289 (0·21%) individuals including 620 with > 20 mmol/L. In multivariable analyses, a triglyceride concentration > 20 mmol/L was associated with very high risk for acute pancreatitis (Hazard ratio (HR) 13·55 (95% CI 9·15-20·06)); chronic pancreatitis (HR 25·19 (14·91-42·55)); and high risk for diabetes (HR 5·28 (4·51-6·18)) and all-cause mortality (HR 3·62 (2·82-4·65)) when compared to the reference category of ≤ 1·7 mmol/L. An association with myocardial infarction, however, was only observed for more moderate hypertriglyceridaemia between 1.7 and 10 mmol/L. We found a risk interaction with age, with higher risks for all outcomes including mortality among those ≤ 40 years compared to > 40 years. CONCLUSIONS We highlight an exponential association between severe hypertriglyceridaemia and risk of incident acute and chronic pancreatitis, new diabetes, and mortality, especially at younger ages, but not for myocardial infarction for which only moderate hypertriglyceridemia conferred risk.
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Affiliation(s)
- Riyaz S Patel
- Institute of Cardiovascular Sciences, University College London, 222 Euston Rd, London, NW1 2DA, UK.
- London Biomedical Research Centre, NIHR University College, University College London and University College London Hospitals NHS Foundation Trust, London, UK.
- UCL BHF Research Accelerator, UCL, London, UK.
| | - Laura Pasea
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Handrean Soran
- Department of Endocrinology, Diabetes and Metabolism, Manchester Royal Infirmary, Manchester, UK
| | - Paul Downie
- Department of Clinical Biochemistry, Bristol Royal Infirmary, Bristol, UK
| | - Richard Jones
- Global Medical Affairs, Akcea Therapeutics, Reading, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Sciences, University College London, 222 Euston Rd, London, NW1 2DA, UK
- London Biomedical Research Centre, NIHR University College, University College London and University College London Hospitals NHS Foundation Trust, London, UK
- UCL BHF Research Accelerator, UCL, London, UK
| | - Dermot Neely
- Academic Health Science Network North East and North Cumbria (AHSN), Newcastle, UK
| | - Spiros Denaxas
- UCL BHF Research Accelerator, UCL, London, UK
- Health Data Research UK London, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Harry Hemingway
- UCL BHF Research Accelerator, UCL, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
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23
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Godec TR, Bromage DI, Pujades‐Rodriguez M, Cannatà A, Gonzalez‐Izquierdo A, Denaxas S, Hemingway H, Shah AM, Yellon DM, McDonagh TA. Cardiovascular outcomes associated with treatment of type 2 diabetes in patients with ischaemic heart failure. ESC Heart Fail 2022; 9:1608-1615. [PMID: 35322592 PMCID: PMC9065866 DOI: 10.1002/ehf2.13910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/31/2022] [Accepted: 03/11/2022] [Indexed: 12/31/2022] Open
Abstract
AIM The optimal strategy for diabetes control in patients with heart failure (HF) following myocardial infarction (MI) remains unknown. Metformin, a guideline-recommended therapy for patients with chronic HF and type 2 diabetes mellitus (T2DM), is associated with reduced mortality and HF hospitalizations. However, worse outcomes have been reported when used at the time of MI. We compared outcomes of patients with T2DM and HF of ischaemic aetiology according to antidiabetic treatment. METHODS AND RESULTS This study used linked data from primary care, hospital admissions, and death registries for 4.7 million inhabitants in England, as part of the CALIBER resource. The primary endpoint was a composite of cardiovascular mortality and HF hospitalization. The secondary endpoints were the individual components of the primary endpoint and all-cause mortality. To evaluate the effect of temporal changes in diabetes treatment, antidiabetic medication was included as time-dependent covariates in survival analyses. The study included 1172 patients with T2DM and prior MI and incident HF between 3 January 1998 and 26 February 2010. Five hundred and ninety-six patients had the primary outcome over median follow-up of 2.53 (IQR: 0.98-4.92) years. Adjusted analyses showed a reduced hazard of the composite endpoint for exposure to all antidiabetic medication with hazard ratios (HRs) of 0.50 [95% confidence interval (CI): 0.42-0.59], 0.66 (95% CI: 0.55-0.80), and 0.53 (95% CI: 0.43-0.65), respectively. A similar effect was seen for all-cause mortality [HRs of 0.43 (95% CI: 0.35-0.52), 0.57 (95% CI: 0.46-0.70), and 0.34 (95% CI: 0.27-0.43), respectively]. CONCLUSIONS When considering changes in antidiabetic treatment over time, all drug classes were associated with reduced risk of cardiovascular mortality and HF hospitalization.
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Affiliation(s)
- Thomas R. Godec
- Department of Medical Statistics, Faculty of Epidemiology and Population HealthThe London School of Hygiene & Tropical MedicineLondonUK
| | - Daniel I. Bromage
- School of Cardiovascular Medicine and SciencesKing's College London British Heart Foundation Centre of Excellence, James Black Centre125 Coldharbour LaneLondonSE5 9NUUK
| | | | - Antonio Cannatà
- School of Cardiovascular Medicine and SciencesKing's College London British Heart Foundation Centre of Excellence, James Black Centre125 Coldharbour LaneLondonSE5 9NUUK
| | - Arturo Gonzalez‐Izquierdo
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
- The National Institute for Health Research University College London Hospitals Biomedical Research CentreUniversity College LondonLondonUK
| | - Spiros Denaxas
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
- The National Institute for Health Research University College London Hospitals Biomedical Research CentreUniversity College LondonLondonUK
| | - Harry Hemingway
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
- The National Institute for Health Research University College London Hospitals Biomedical Research CentreUniversity College LondonLondonUK
| | - Ajay M. Shah
- School of Cardiovascular Medicine and SciencesKing's College London British Heart Foundation Centre of Excellence, James Black Centre125 Coldharbour LaneLondonSE5 9NUUK
| | - Derek M. Yellon
- The Hatter Cardiovascular InstituteUniversity College LondonLondonUK
| | - Theresa A. McDonagh
- School of Cardiovascular Medicine and SciencesKing's College London British Heart Foundation Centre of Excellence, James Black Centre125 Coldharbour LaneLondonSE5 9NUUK
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24
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Patel RS, Denaxas S, Howe LJ, Eggo RM, Shah AD, Allen NE, Danesh J, Hingorani A, Sudlow C, Hemingway H. Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank. PLoS One 2022; 17:e0264828. [PMID: 35381005 PMCID: PMC8982857 DOI: 10.1371/journal.pone.0264828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/17/2022] [Indexed: 12/05/2022] Open
Abstract
IMPORTANCE A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility. OBJECTIVE To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB). DESIGN Case-control and cohort study. SETTING Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping. PARTICIPANTS We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated. EXPOSURE CAD Phenotypes. MAIN OUTCOMES AND MEASURES Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD. RESULTS The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/). CONCLUSIONS An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.
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Affiliation(s)
- Riyaz S. Patel
- Institute of Cardiovascular Sciences, University College London, London, United Kingdom
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Spiros Denaxas
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Laurence J. Howe
- Institute of Cardiovascular Sciences, University College London, London, United Kingdom
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Rosalind M. Eggo
- Institute of Health Informatics, University College London, London, United Kingdom
- Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Health Data Research UK London, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Anoop D. Shah
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Naomi E. Allen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- UK Biobank Ltd, Stockport, United Kingdom
| | - John Danesh
- Health Data Research UK Cambridge, Hinxton, United Kingdom
- Department of Public Health and Primary Care, Cambridge University, Cambridge, United Kingdom
| | - Aroon Hingorani
- Institute of Cardiovascular Sciences, University College London, London, United Kingdom
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Cathie Sudlow
- Health Data Research UK Scotland, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- HDR UK London, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Harry Hemingway
- NIHR University College London Biomedical Research Centre, University College London and University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
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25
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Jordan KP, Rathod‐Mistry T, Bailey J, Chen Y, Clarson L, Denaxas S, Hayward RA, Hemingway H, van der Windt DA, Mamas MA. Long-Term Cardiovascular Risk and Management of Patients Recorded in Primary Care With Unattributed Chest Pain: An Electronic Health Record Study. J Am Heart Assoc 2022; 11:e023146. [PMID: 35301875 PMCID: PMC9075433 DOI: 10.1161/jaha.121.023146] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Background Most adults presenting with chest pain will not receive a diagnosis and be recorded with unattributed chest pain. The objective was to assess if they have increased risk of cardiovascular disease compared with those with noncoronary chest pain and determine whether investigations and interventions are targeted at those at highest risk. Methods and Results We used records from general practices in England linked to hospitalization and mortality information. The study population included patients aged 18 years or over with a new record of chest pain with a noncoronary cause or unattributed between 2002 and 2018, and no cardiovascular disease recorded up to 6 months (diagnostic window) afterward. We compared risk of a future cardiovascular event by type of chest pain, adjusting for cardiovascular risk factors and alternative explanations for chest pain. We determined prevalence of cardiac diagnostic investigations and preventative medication during the diagnostic window in patients with estimated cardiovascular risk ≥10%. There were 375 240 patients with unattributed chest pain (245 329 noncoronary chest pain). There was an increased risk of cardiovascular events for patients with unattributed chest pain, highest in the first year (hazard ratio, 1.25 [95% CI, 1.21-1.29]), persistent up to 10 years. Patients with unattributed chest pain had consistently increased risk of myocardial infarction over time but no increased risk of stroke. Thirty percent of patients at higher risk were prescribed lipid-lowering medication. Conclusions Patients presenting to primary care with unattributed chest pain are at increased risk of cardiovascular events. Primary prevention to reduce cardiovascular events appears suboptimal in those at higher risk.
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Affiliation(s)
| | | | - James Bailey
- School of MedicineKeele UniversityKeeleUnited Kingdom
| | - Ying Chen
- School of MedicineKeele UniversityKeeleUnited Kingdom
- Department of Health and Environmental SciencesXi'an Jiaotong–Liverpool UniversitySuzhouChina
| | - Lorna Clarson
- School of MedicineKeele UniversityKeeleUnited Kingdom
| | - Spiros Denaxas
- Institute of Health InformaticsUniversity College LondonLondonUnited Kingdom
- Health Data Research UKUniversity College LondonLondonUnited Kingdom
| | | | - Harry Hemingway
- Institute of Health InformaticsUniversity College LondonLondonUnited Kingdom
- Health Data Research UKUniversity College LondonLondonUnited Kingdom
- The National Institute for Health ResearchUniversity College London Hospitals Biomedical Research CentreLondonUnited Kingdom
| | | | - Mamas A. Mamas
- Keele Cardiovascular Research GroupSchool of MedicineKeele UniversityKeeleUnited Kingdom
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26
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Zhang R, Mamza JB, Morris T, Godfrey G, Asselbergs FW, Denaxas S, Hemingway H, Banerjee A. Correction to: Lifetime risk of cardiovascular-renal disease in type 2 diabetes: a population-based study in 473,399 individuals. BMC Med 2022; 20:121. [PMID: 35317796 PMCID: PMC8941726 DOI: 10.1186/s12916-022-02308-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Ruiqi Zhang
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Jil Billy Mamza
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Tamsin Morris
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - George Godfrey
- Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK.
- Health Data Research UK London, University College London, London, UK.
- Barts Health NHS Trust, London, UK.
- University College London Hospitals NHS Trust, London, UK.
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27
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Sau A, Kaura A, Ahmed A, Patel KHK, Li X, Mulla A, Glampson B, Panoulas V, Davies J, Woods K, Gautama S, Shah AD, Elliott P, Hemingway H, Williams B, Asselbergs FW, Melikian N, Peters NS, Shah AM, Perera D, Kharbanda R, Patel RS, Channon KM, Mayet J, Ng FS. Prognostic Significance of Ventricular Arrhythmias in 13 444 Patients With Acute Coronary Syndrome: A Retrospective Cohort Study Based on Routine Clinical Data (NIHR Health Informatics Collaborative VA-ACS Study). J Am Heart Assoc 2022; 11:e024260. [PMID: 35258317 PMCID: PMC9075290 DOI: 10.1161/jaha.121.024260] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/07/2021] [Accepted: 01/06/2022] [Indexed: 12/19/2022]
Abstract
Background A minority of acute coronary syndrome (ACS) cases are associated with ventricular arrhythmias (VA) and/or cardiac arrest (CA). We investigated the effect of VA/CA at the time of ACS on long-term outcomes. Methods and Results We analyzed routine clinical data from 5 National Health Service trusts in the United Kingdom, collected between 2010 and 2017 by the National Institute for Health Research Health Informatics Collaborative. A total of 13 444 patients with ACS, 376 (2.8%) of whom had concurrent VA, survived to hospital discharge and were followed up for a median of 3.42 years. Patients with VA or CA at index presentation had significantly increased risks of subsequent VA during follow-up (VA group: adjusted hazard ratio [HR], 4.15 [95% CI, 2.42-7.09]; CA group: adjusted HR, 2.60 [95% CI, 1.23-5.48]). Patients who suffered a CA in the context of ACS and survived to discharge also had a 36% increase in long-term mortality (adjusted HR, 1.36 [95% CI, 1.04-1.78]), although the concurrent diagnosis of VA alone during ACS did not affect all-cause mortality (adjusted HR, 1.03 [95% CI, 0.80-1.33]). Conclusions Patients who develop VA or CA during ACS who survive to discharge have increased risks of subsequent VA, whereas those who have CA during ACS also have an increase in long-term mortality. These individuals may represent a subgroup at greater risk of subsequent arrhythmic events as a result of intrinsically lower thresholds for developing VA.
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Affiliation(s)
- Arunashis Sau
- National Heart and Lung InstituteImperial College LondonLondonUK
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | - Amit Kaura
- National Heart and Lung InstituteImperial College LondonLondonUK
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | - Amar Ahmed
- National Heart and Lung InstituteImperial College LondonLondonUK
| | | | - Xinyang Li
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Abdulrahim Mulla
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | - Benjamin Glampson
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | | | - Jim Davies
- National Institute for Health Research Oxford Biomedical Research CentreUniversity of Oxford and Oxford University Hospitals NHS Foundation TrustOxfordUK
| | - Kerrie Woods
- National Institute for Health Research Oxford Biomedical Research CentreUniversity of Oxford and Oxford University Hospitals NHS Foundation TrustOxfordUK
| | - Sanjay Gautama
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | - Anoop D. Shah
- National Institute for Health Research University College London Biomedical Research CentreUniversity College London and University College London Hospitals NHS Foundation TrustLondonUK
| | - Paul Elliott
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
- Health Data Research UKLondon Substantive SiteLondonUK
| | - Harry Hemingway
- National Institute for Health Research University College London Biomedical Research CentreUniversity College London and University College London Hospitals NHS Foundation TrustLondonUK
- Health Data Research UKLondon Substantive SiteLondonUK
| | - Bryan Williams
- National Institute for Health Research University College London Biomedical Research CentreUniversity College London and University College London Hospitals NHS Foundation TrustLondonUK
| | - Folkert W. Asselbergs
- National Institute for Health Research University College London Biomedical Research CentreUniversity College London and University College London Hospitals NHS Foundation TrustLondonUK
| | - Narbeh Melikian
- National Institute for Health Research King’s Biomedical Research CentreKing’s College London and King’s College Hospital NHS Foundation TrustLondonUK
| | | | - Ajay M. Shah
- National Institute for Health Research King’s Biomedical Research CentreKing’s College London and King’s College Hospital NHS Foundation TrustLondonUK
| | - Divaka Perera
- National Institute for Health Research King’s Biomedical Research CentreKing’s College London and Guy’s and St Thomas' NHS Foundation TrustLondonUK
| | - Rajesh Kharbanda
- National Institute for Health Research Oxford Biomedical Research CentreUniversity of Oxford and Oxford University Hospitals NHS Foundation TrustOxfordUK
| | - Riyaz S. Patel
- National Institute for Health Research University College London Biomedical Research CentreUniversity College London and University College London Hospitals NHS Foundation TrustLondonUK
| | - Keith M. Channon
- National Institute for Health Research Oxford Biomedical Research CentreUniversity of Oxford and Oxford University Hospitals NHS Foundation TrustOxfordUK
| | - Jamil Mayet
- National Heart and Lung InstituteImperial College LondonLondonUK
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
| | - Fu Siong Ng
- National Heart and Lung InstituteImperial College LondonLondonUK
- National Institute for Health Research Imperial Biomedical Research CentreImperial College London and Imperial College Healthcare NHS TrustLondonUK
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28
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Banerjee A, Pasea L, Chung S, Direk K, Asselbergs FW, Grobbee DE, Kotecha D, Anker SD, Dyszynski T, Tyl B, Denaxas S, Lumbers RT, Hemingway H. A population-based study of 92 clinically recognized risk factors for heart failure: co-occurrence, prognosis and preventive potential. Eur J Heart Fail 2022; 24:466-480. [PMID: 34969173 PMCID: PMC9305958 DOI: 10.1002/ejhf.2417] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/14/2021] [Accepted: 12/28/2021] [Indexed: 11/11/2022] Open
Abstract
AIMS Primary prevention strategies for heart failure (HF) have had limited success, possibly due to a wide range of underlying risk factors (RFs). Systematic evaluations of the prognostic burden and preventive potential across this wide range of risk factors are lacking. We aimed at estimating evidence, prevalence and co-occurrence for primary prevention and impact on prognosis of RFs for incident HF. METHODS AND RESULTS We systematically reviewed trials and observational evidence of primary HF prevention across 92 putative aetiologic RFs for HF identified from US and European clinical practice guidelines. We identified 170 885 individuals aged ≥30 years with incident HF from 1997 to 2017, using linked primary and secondary care UK electronic health records (EHR) and rule-based phenotypes (ICD-10, Read Version 2, OPCS-4 procedure and medication codes) for each of 92 RFs. Only 10/92 factors had high quality observational evidence for association with incident HF; 7 had effective randomized controlled trial (RCT)-based interventions for HF prevention (RCT-HF), and 6 for cardiovascular disease prevention, but not HF (RCT-CVD), and the remainder had no RCT-based preventive interventions (RCT-0). We were able to map 91/92 risk factors to EHR using 5961 terms, and 88/91 factors were represented by at least one patient. In the 5 years prior to HF diagnosis, 44.3% had ≥4 RFs. By RCT evidence, the most common RCT-HF RFs were hypertension (48.5%), stable angina (34.9%), unstable angina (16.8%), myocardial infarction (15.8%), and diabetes (15.1%); RCT-CVD RFs were smoking (46.4%) and obesity (29.9%); and RCT-0 RFs were atrial arrhythmias (17.2%), cancer (16.5%), heavy alcohol intake (14.9%). Mortality at 1 year varied across all 91 factors (lowest: pregnancy-related hormonal disorder 4.2%; highest: phaeochromocytoma 73.7%). Among new HF cases, 28.5% had no RCT-HF RFs and 38.6% had no RCT-CVD RFs. 15.6% had either no RF or only RCT-0 RFs. CONCLUSION One in six individuals with HF have no recorded RFs or RFs without trials. We provide a systematic map of primary preventive opportunities across a wide range of RFs for HF, demonstrating a high burden of co-occurrence and the need for trials tackling multiple RFs.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health InformaticsUniversity College LondonLondonUK
- University College London Hospitals NHS TrustLondonUK
- Barts Health NHS TrustThe Royal London HospitalLondonUK
| | - Laura Pasea
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Sheng‐Chia Chung
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Kenan Direk
- Institute of Health InformaticsUniversity College LondonLondonUK
- UCL Energy InstituteLondonUK
| | - Folkert W. Asselbergs
- Institute of Health InformaticsUniversity College LondonLondonUK
- University College London Hospitals NHS TrustLondonUK
- Health Data Research UKLondonUK
- Department of CardiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Diederick E. Grobbee
- Julius Center Research Program Cardiovascular EpidemiologyUtrecht UniversityUtrechtThe Netherlands
| | - Dipak Kotecha
- Department of CardiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
- Institute of Cardiovascular SciencesUniversity of BirminghamBirminghamUK
- Health Data Research UK MidlandsUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
| | - Stefan D. Anker
- Department of CardiologyCharité Campus Virchow‐KlinikumBerlinGermany
| | - Tomasz Dyszynski
- Bayer AG, Medical Affairs & Pharmacovigilance, Pharmaceuticals TG CardioThrombosis & Hemophilia Building M084BerlinGermany
| | - Benoît Tyl
- Center for Therapeutic Innovation, Cardiovascular and Metabolic DiseaseInstitut de Recherches Internationales ServierSuresnes CedexFrance
| | - Spiros Denaxas
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UKLondonUK
| | - R. Thomas Lumbers
- Institute of Health InformaticsUniversity College LondonLondonUK
- University College London Hospitals NHS TrustLondonUK
- Health Data Research UKLondonUK
| | - Harry Hemingway
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UKLondonUK
- National Institute for Health Research University College London Hospitals Biomedical Research CentreLondonUK
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Denaxas S, Liu G, Feng Q, Fatemifar G, Bastarache L, Kerchberger EV, Hingorani AD, Lumbers T, Peterson JF, Wei WQ, Hemingway H. Mapping the Read2/CTV3 controlled clinical terminologies to Phecodes in UK Biobank primary care electronic health records: implementation and evaluation. AMIA Annu Symp Proc 2022; 2021:362-371. [PMID: 35308936 PMCID: PMC8861677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Objective: To establish and validate mappings between primary care clinical terminologies (Read Version 2, Clinical Terms Version 3) and Phecodes. Methods: We processed 123,662,421 primary care events from 230,096 UK Biobank (UKB) participants. We assessed the validity of the primary care-derived Phecodes by conducting PheWAS analyses for seven pre-selected SNPs in the UKB and compared with estimates from BioVU. Results: We mapped 92% of Read2 (n=10,834) and 91% of CTV3 (n=21,988) to 1,449 and 1,490 Phecodes. UKB PheWAS using Phecodes from primary care EHR and hospitalizations replicated all (n=22) previously-reported genotype-phenotype associations. When limiting Phecodes to primary care EHR, replication was 81% (n=18). Conclusion: We introduced a first version of mappings from Read2/CTV3 to Phecodes. The reference list of diseases provided by Phecodes can be extended, enabling researchers to leverage primary care EHR for high-throughput discovery research.
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Affiliation(s)
- Spiros Denaxas
- University College London, London, UK
- Health Data Research UK, London, UK
- BHF Research Accelerator, London, UK
- The Alan Turing Institute, London, UK
- NIHR UCLH BRC, London, UK
| | - Ge Liu
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ghazaleh Fatemifar
- Vanderbilt University Medical Center, Nashville, TN, USA
- Health Data Research UK, London, UK
| | | | | | - Aroon D Hingorani
- University College London, London, UK
- Health Data Research UK, London, UK
- BHF Research Accelerator, London, UK
| | - Tom Lumbers
- University College London, London, UK
- Health Data Research UK, London, UK
| | | | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Harry Hemingway
- University College London, London, UK
- Health Data Research UK, London, UK
- BHF Research Accelerator, London, UK
- NIHR UCLH BRC, London, UK
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Dashtban M, Mizani M, Gonzalez-Izquierdo A, Corbett R, Quint J, Denaxas S, Mamza J, Morris T, Hemingway H, Banerjee A. POS-283 HIERARCHICAL CLUSTERING FOR SUBTYPE DISCOVERY OF INCIDENT CHRONIC KIDNEY DISEASE FROM LARGE LONGITUDINAL ELECTRONIC HEALTH RECORDS. Kidney Int Rep 2022. [DOI: 10.1016/j.ekir.2022.01.303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Banerjee A, Chen S, Pasea L, Lai AG, Katsoulis M, Denaxas S, Nafilyan V, Williams B, Wong WK, Bakhai A, Khunti K, Pillay D, Noursadeghi M, Wu H, Pareek N, Bromage D, McDonagh TA, Byrne J, Teo JTH, Shah AM, Humberstone B, Tang LV, Shah ASV, Rubboli A, Guo Y, Hu Y, Sudlow CLM, Lip GYH, Hemingway H. Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic. Eur J Prev Cardiol 2021; 28:1599-1609. [PMID: 33611594 PMCID: PMC7928969 DOI: 10.1093/eurjpc/zwaa155] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 12/21/2022]
Abstract
AIMS Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both 'direct', through infection, and 'indirect', through changes in healthcare. METHODS AND RESULTS We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0).Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. CONCLUSION Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
- Department of Cardiology, Barts Health NHS Trust, Royal London Hospital, Whitechapel Road, London, UK, E1 1BB
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK, NW1 2BU
| | - Suliang Chen
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
| | - Laura Pasea
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
| | - Alvina G Lai
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
| | - Michail Katsoulis
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
| | - Vahe Nafilyan
- Office for National Statistics. 1 Drummond Gate, Pimlico, London, UK, SW1V 2QQ
| | - Bryan Williams
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK, NW1 2BU
- Institute of Cardiovascular Science, University College London, London, UK, WC1E 6BT
- University College London Hospitals NIHR Biomedical Research Centre, Maple House, 1st Floor, 149 Tottenham Court Road, London, UK, W1T 7DN
| | - Wai Keong Wong
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK, NW1 2BU
| | - Ameet Bakhai
- Department of Cardiology, Royal Free Hospital, Pond Street, London, UK, NW3 2QG
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Rd, Leicester, UK, LE5 4PW
| | - Deenan Pillay
- Division of Infection and Immunity, UCL Cruciform Building, University College London, Gower Street, London, UK, WC1E 6BT
| | - Mahdad Noursadeghi
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK, NW1 2BU
- Division of Infection and Immunity, UCL Cruciform Building, University College London, Gower Street, London, UK, WC1E 6BT
| | - Honghan Wu
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
- School of Computer and Software, Najing University of Information Science and Technology, Ningliu Road, Nanjing, Jiangsu Province, P.R.C. 210044, China
| | - Nilesh Pareek
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
| | - Daniel Bromage
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
- Kings College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, Strand, London WC2R 2LS. UK
| | - Theresa A McDonagh
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
- Kings College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, Strand, London WC2R 2LS. UK
| | - Jonathan Byrne
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
| | - James T H Teo
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
| | - Ajay M Shah
- Kings College Hospital NHS Foundation Trust, Denmark Hill, Brixton, London, UK, SE5 9RS
- Kings College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, Strand, London WC2R 2LS. UK
| | - Ben Humberstone
- Office for National Statistics. 1 Drummond Gate, Pimlico, London, UK, SW1V 2QQ
| | - Liang V Tang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Anoop S V Shah
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, 47 Little France Crescent Edinburgh, UK. EH16 4TJ
| | - Andrea Rubboli
- Division of Cardiology, Ospedale S. Maria delle Croci, Viale Randi 5, 48121, Ravenna. Italy
| | - Yutao Guo
- PLA General Hospital, 28 Fuxing Road, Beijing, Haidian District, Beijing, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Cathie L M Sudlow
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, 9 Little France Road, Edinburgh BioQuarter City, Edinburgh, UK, EH16 4UX
- BHF Data Science Centre, Health Data Research, 215 Euston Road, London, UK, NW1 2BE
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool,William Henry Duncan Building, 6 W Derby Street, Liverpool, UK, L7 8TX
- Liverpool Heart & Chest Hospital, Thomas Drive, Liverpool, UK, L14 3PE
- Department of Clinical Medicine, Aalborg Thrombosis Research Unit, Aalborg University, Søndre Skovvej 15, Forskningens Hus 9000, Aalborg, Denmark
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK, NW1 1DA
- Health Data Research UK, Gibbs Building, 215 Euston Road, London, UK, NW1 2BE
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Banerjee A, Pasea L, Manohar S, Lai AG, Hemingway E, Sofer I, Katsoulis M, Sood H, Morris A, Cake C, Fitzpatrick NK, Williams B, Denaxas S, Hemingway H. 'What is the risk to me from COVID-19?': Public involvement in providing mortality risk information for people with 'high-risk' conditions for COVID-19 (OurRisk.CoV). Clin Med (Lond) 2021; 21:e620-e628. [PMID: 34862222 DOI: 10.7861/clinmed.2021-0386] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Patients and public have sought mortality risk information throughout the pandemic, but their needs may not be served by current risk prediction tools. Our mixed methods study involved: (1) systematic review of published risk tools for prognosis, (2) provision and patient testing of new mortality risk estimates for people with high-risk conditions and (3) iterative patient and public involvement and engagement with qualitative analysis. Only one of 53 (2%) previously published risk tools involved patients or the public, while 11/53 (21%) had publicly accessible portals, but all for use by clinicians and researchers.Among people with a wide range of underlying conditions, there has been sustained interest and engagement in accessible and tailored, pre- and postpandemic mortality information. Informed by patient feedback, we provide such information in 'five clicks' (https://covid19-phenomics.org/OurRiskCoV.html), as context for decision making and discussions with health professionals and family members. Further development requires curation and regular updating of NHS data and wider patient and public engagement.
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Affiliation(s)
- Amitava Banerjee
- University College London, London, UK, honorary consultant cardiologist, University College London Hospitals NHS Trust, London, UK, and honorary consultant cardiologist, Barts Health NHS Trust, London, UK
| | | | | | - Alvina G Lai
- University College London, London, UK, and associate, Health Data Research UK, London, UK
| | | | | | | | - Harpreet Sood
- Health Education England, London, UK, and general practitioner, Hurley Group Practice, London, UK
| | | | | | - Natalie K Fitzpatrick
- University College London, London, UK, and associate, Health Data Research UK, London, UK
| | - Bryan Williams
- University College London Hospitals NHS Trust, London, UK, professor of medicine, University College London, London, UK, and director, UCL Hospitals NIHR Biomedical Research Centre
| | - Spiros Denaxas
- University College London, London, UK, associate, Health Data Research UK, and research fellow, Alan Turing Institute, London, UK
| | - Harry Hemingway
- University College London, London, UK, research director, Health Data Research UK, London, UK, and director of healthcare informatics, genomics/omics, data science, UCL Hospitals NIHR Biomedical Research Centre, London, UK
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Rathod-Mistry T, Mamas M, Bailey J, Chen Y, Clarson L, Denaxas S, Hayward R, Hemingway H, Van Der Windt D, Jordan K. Comparison of risk factors for coronary event in people with unattributed and non-coronary chest pain: an electronic health record cohort study. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.1350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Patients presenting to primary care with chest pain are often not given a cause. Patients with such unattributed chest pain have an increased risk of future cardiovascular disease (CVD) compared to patients with diagnosed non-coronary chest pain. It is unknown whether risk factors for CVD determined in the general population are the same for the population with unattributed or non-coronary chest pain.
Purpose
To determine if key risk factors for a coronary event in patients with unattributed chest pain are similar to those for patients with non-coronary chest pain and previously identified in the general population.
Methods
The study used primary care information from the Clinical Practice Research Datalink Aurum linked to hospital inpatient and mortality data. Patients aged ≥18 years with an incident record of unattributed or non-coronary chest pain in 2002–2018 and no diagnosis of CVD were included. We included as potential risk factors those established for CVD in the general population and non-coronary explanations for chest pain. Flexible parametric models estimated hazard ratios (95% confidence intervals (CI)) between factors and incident coronary event (defined as myocardial infarction, angina, coronary heart disease, percutaneous intervention, and coronary artery bypass graft surgery).
Results
There were 375,240 patients with unattributed chest pain (53% female: mean age 49; 47% male: mean age 47) and 245,329 patients with non-coronary chest pain (58% female: mean age 47; 42% male: mean age 44). Median duration of follow-up was 5 years. In the unattributed chest pain group, there were 111 (95% CI: 109, 112) and 140 (138, 142) coronary events per 10,000 person-years in females and males, respectively. Lower rates of coronary event were observed in the non-coronary chest pain group (females: 73 (72, 75); males: 96 (94, 98)). Within females (Figure), in both chest pain groups the strongest risk factors were type I and type II diabetes, atrial fibrillation, and hypertension whereas no associations were observed for migraine and chronic kidney disease. Whilst alternative explanations for non-coronary chest pain also increased the risk of coronary events, associations were less strong than for established general population risk factors. Similar findings were found in males although family history of coronary event was a stronger risk factor in the non-coronary chest pain group compared to the unattributed chest pain group.
Conclusions
The pool of factors found to increase the risk of coronary events in patients presenting with recorded unattributed or non-coronary chest pain are similar but not identical to those identified for the general population. Further research is needed to develop prognostic models to identify patients at the most risk of a coronary event as models developed in the general population are unlikely to be applicable given the increased underlying risk of coronary events in these populations.
Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): Study funded by the British Heart Foundation, reference PG/19/46/34307. KJ also supported by matched funding awarded to the NIHR Applied Research Collaboration (West Midlands). Risk factors for coronary event
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Affiliation(s)
- T Rathod-Mistry
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - M Mamas
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - J Bailey
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - Y Chen
- Xi'an Jiaotong - Liverpool University, Suzhou, China
| | - L Clarson
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - S Denaxas
- University College London, Health Data Research UK, London, United Kingdom
| | - R Hayward
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - H Hemingway
- University College London, Health Data Research UK, London, United Kingdom
| | - D Van Der Windt
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
| | - K Jordan
- Keele University, Primary Care Centre Versus Arthritis, School of Medicine, Keele, United Kingdom
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Lai AG, Chang WH, Parisinos CA, Katsoulis M, Blackburn RM, Shah AD, Nguyen V, Denaxas S, Davey Smith G, Gaunt TR, Nirantharakumar K, Cox MP, Forde D, Asselbergs FW, Harris S, Richardson S, Sofat R, Dobson RJB, Hingorani A, Patel R, Sterne J, Banerjee A, Denniston AK, Ball S, Sebire NJ, Shah NH, Foster GR, Williams B, Hemingway H. An informatics consult approach for generating clinical evidence for treatment decisions. BMC Med Inform Decis Mak 2021; 21:281. [PMID: 34641870 PMCID: PMC8506488 DOI: 10.1186/s12911-021-01638-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/27/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis. METHODS We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems. RESULTS We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39-1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49-0.76) and ischaemic stroke (HR = 0.27, CI 0.08-0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results. CONCLUSION We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on 'patients like me'. We identify the key challenges in offering such an Informatics Consult as a service.
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Affiliation(s)
- Alvina G Lai
- Institute of Health Informatics, University College London, London, UK.
- Health Data Research UK, London, UK.
| | - Wai Hoong Chang
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | | | - Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK
| | - Ruth M Blackburn
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Anoop D Shah
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Vincent Nguyen
- Institute of Health Informatics, University College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- The Alan Turing Institute, London, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Krishnarajah Nirantharakumar
- Health Data Research UK, London, UK
- Institute of Applies Health Research, University of Birmingham, Birmingham, UK
| | - Murray P Cox
- Statistics and Bioinformatics Group, School of Fundamental Sciences, Massey University, Palmerston North, New Zealand
| | - Donall Forde
- Public Health Wales, University Hospital of Wales, Cardiff, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Institute of Cardiovascular Science, University College London, London, UK
| | - Steve Harris
- University College London Hospitals NHS Trust, London, UK
| | - Sylvia Richardson
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
| | - Richard J B Dobson
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Aroon Hingorani
- Health Data Research UK, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
| | - Riyaz Patel
- Institute of Cardiovascular Science, University College London, London, UK
| | - Jonathan Sterne
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK
| | - Alastair K Denniston
- Health Data Research UK, London, UK
- University Hospitals Birmingham NHSFT, Birmingham, UK
| | - Simon Ball
- Health Data Research UK, London, UK
- University Hospitals Birmingham NHSFT, Birmingham, UK
| | - Neil J Sebire
- UCL Great Ormond Street Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Nigam H Shah
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Graham R Foster
- Barts Liver Centre, Blizard Institute, Queen Mary University of London, London, UK
| | - Bryan Williams
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
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35
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Katsoulis M, Lai AG, Diaz-Ordaz K, Gomes M, Pasea L, Banerjee A, Denaxas S, Tsilidis K, Lagiou P, Misirli G, Bhaskaran K, Wannamethee G, Dobson R, Batterham RL, Kipourou DK, Lumbers RT, Wen L, Wareham N, Langenberg C, Hemingway H. Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records. Lancet Diabetes Endocrinol 2021; 9:681-694. [PMID: 34481555 PMCID: PMC8440227 DOI: 10.1016/s2213-8587(21)00207-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/17/2021] [Accepted: 07/20/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR). METHODS In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions. FINDINGS We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period. INTERPRETATION A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care. FUNDING The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.
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Affiliation(s)
- Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
| | - Alvina G Lai
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Karla Diaz-Ordaz
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Manuel Gomes
- Department of Applied Health Research, University College London, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; University College London Hospitals NHS Trust, London, UK; Barts Health NHS Trust, The Royal London Hospital, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; Alan Turing Institute, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Kostas Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Pagona Lagiou
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Goya Wannamethee
- Department of Primary Care and Population Health, University College London, London, UK
| | - Richard Dobson
- Health Data Research UK, University College London, London, UK; Institute of Health Informatics, University College London, London, UK; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel L Batterham
- Centre for Obesity Research, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK; University College London Hospitals Bariatric Centre for Weight Management and Metabolic Surgery, London, UK
| | - Dimitra-Kleio Kipourou
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Lan Wen
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health, Charité-University Medicine Berlin, Berlin, Germany
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
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36
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Lumbers RT, Shah S, Lin H, Czuba T, Henry A, Swerdlow DI, Mälarstig A, Andersson C, Verweij N, Holmes MV, Ärnlöv J, Svensson P, Hemingway H, Sallah N, Almgren P, Aragam KG, Asselin G, Backman JD, Biggs ML, Bloom HL, Boersma E, Brandimarto J, Brown MR, Brunner-La Rocca HP, Carey DJ, Chaffin MD, Chasman DI, Chazara O, Chen X, Chen X, Chung JH, Chutkow W, Cleland JGF, Cook JP, de Denus S, Dehghan A, Delgado GE, Denaxas S, Doney AS, Dörr M, Dudley SC, Engström G, Esko T, Fatemifar G, Felix SB, Finan C, Ford I, Fougerousse F, Fouodjio R, Ghanbari M, Ghasemi S, Giedraitis V, Giulianini F, Gottdiener JS, Gross S, Guðbjartsson DF, Gui H, Gutmann R, Haggerty CM, van der Harst P, Hedman ÅK, Helgadottir A, Hillege H, Hyde CL, Jacob J, Jukema JW, Kamanu F, Kardys I, Kavousi M, Khaw KT, Kleber ME, Køber L, Koekemoer A, Kraus B, Kuchenbaecker K, Langenberg C, Lind L, Lindgren CM, London B, Lotta LA, Lovering RC, Luan J, Magnusson P, Mahajan A, Mann D, Margulies KB, Marston NA, März W, McMurray JJV, Melander O, Melloni G, Mordi IR, Morley MP, Morris AD, Morris AP, Morrison AC, Nagle MW, Nelson CP, Newton-Cheh C, Niessner A, Niiranen T, Nowak C, O'Donoghue ML, Owens AT, Palmer CNA, Paré G, Perola M, Perreault LPL, Portilla-Fernandez E, Psaty BM, Rice KM, Ridker PM, Romaine SPR, Roselli C, Rotter JI, Ruff CT, Sabatine MS, Salo P, Salomaa V, van Setten J, Shalaby AA, Smelser DT, Smith NL, Stefansson K, Stender S, Stott DJ, Sveinbjörnsson G, Tammesoo ML, Tardif JC, Taylor KD, Teder-Laving M, Teumer A, Thorgeirsson G, Thorsteinsdottir U, Torp-Pedersen C, Trompet S, Tuckwell D, Tyl B, Uitterlinden AG, Vaura F, Veluchamy A, Visscher PM, Völker U, Voors AA, Wang X, Wareham NJ, Weeke PE, Weiss R, White HD, Wiggins KL, Xing H, Yang J, Yang Y, Yerges-Armstrong LM, Yu B, Zannad F, Zhao F, Wilk JB, Holm H, Sattar N, Lubitz SA, Lanfear DE, Shah S, Dunn ME, Wells QS, Asselbergs FW, Hingorani AD, Dubé MP, Samani NJ, Lang CC, Cappola TP, Ellinor PT, Vasan RS, Smith JG. The genomics of heart failure: design and rationale of the HERMES consortium. ESC Heart Fail 2021; 8:5531-5541. [PMID: 34480422 PMCID: PMC8712846 DOI: 10.1002/ehf2.13517] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/09/2021] [Accepted: 07/05/2021] [Indexed: 12/28/2022] Open
Abstract
Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P < 5 × 10−8 under an additive genetic model. Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.
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Affiliation(s)
- R Thomas Lumbers
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,BHF Research Accelerator, University College London, London, UK
| | - Sonia Shah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Institute of Cardiovascular Science, University College London, London, UK
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.,National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Tomasz Czuba
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Albert Henry
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Daniel I Swerdlow
- Institute of Cardiovascular Science, University College London, London, UK.,Department of Medicine, Imperial College London, London, UK
| | - Anders Mälarstig
- Pfizer Worldwide Research & Development, Cambridge, MA, USA.,Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Charlotte Andersson
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA.,Department of Cardiology, Herlev Gentofte Hospital, Herlev, Denmark
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.,National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.,School of Health and Social Sciences, Dalarna University, Falun, Sweden
| | - Per Svensson
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden.,Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Harry Hemingway
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Neneh Sallah
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,UCL Genetics Institute, University College London, London, UK
| | - Peter Almgren
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Krishna G Aragam
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, USA.,Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Heather L Bloom
- Division of Cardiology, Department of Medicine, Emory University Medical Center, Atlanta, GA, USA
| | - Eric Boersma
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeffrey Brandimarto
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Mark D Chaffin
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Olympe Chazara
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Xing Chen
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - William Chutkow
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - John G F Cleland
- Robertson Centre for Biostatistics & Glasgow Clinical Trials Unit, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK.,National Heart and Lung Institute, Imperial College, London, UK
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Simon de Denus
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, UK.,MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, UK
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK.,The Alan Turing Institute, British Library, London, UK
| | - Alexander S Doney
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Samuel C Dudley
- Cardiovascular Division, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Gunnar Engström
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Tõnu Esko
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK
| | - Stephan B Felix
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
| | - Ian Ford
- Robertson Centre for Biostatistics & Glasgow Clinical Trials Unit, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK
| | - Francoise Fougerousse
- Translational and Clinical Research, Servier Cardiovascular Center for Therapeutic Innovation, Suresnes, France
| | | | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sahar Ghasemi
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - John S Gottdiener
- Department of Medicine, Division of Cardiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stefan Gross
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Daníel F Guðbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Rebecca Gutmann
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | | | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
| | - Åsa K Hedman
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | | | - Hans Hillege
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Craig L Hyde
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Jaison Jacob
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - Frederick Kamanu
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Isabella Kardys
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Lars Køber
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Andrea Koekemoer
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Bill Kraus
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Karoline Kuchenbaecker
- UCL Genetics Institute, University College London, London, UK.,Division of Psychiatry, University College of London, London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Barry London
- Division of Cardiovascular Medicine and Abboud Cardiovascular Research Center, University of Iowa, Iowa City, IA, USA
| | - Luca A Lotta
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ruth C Lovering
- Institute of Cardiovascular Science, University College London, London, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Patrik Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Douglas Mann
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenneth B Margulies
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany.,Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany.,Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - John J V McMurray
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Olle Melander
- Department of Internal Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Giorgio Melloni
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ify R Mordi
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Michael P Morley
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew D Morris
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Christopher Newton-Cheh
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.,Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - Alexander Niessner
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Teemu Niiranen
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
| | - Michelle L O'Donoghue
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anjali T Owens
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin N A Palmer
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
| | | | - Eliana Portilla-Fernandez
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Division of Vascular Medicine and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA.,Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Carolina Roselli
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marc S Sabatine
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Perttu Salo
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jessica van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Alaa A Shalaby
- Division of Cardiology, Department of Medicine, University of Pittsburgh Medical Center and VA Pittsburgh HCS, Pittsburgh, PA, USA
| | - Diane T Smelser
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Nicholas L Smith
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, WA, USA
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Steen Stender
- Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev and Gentofte, Denmark
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | | | - Mari-Liis Tammesoo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexander Teumer
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Guðmundur Thorgeirsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Christian Torp-Pedersen
- Department of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark.,Department of Health, Science and Technology, Aalborg University Hospital, Aalborg, Denmark.,Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.,Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Danny Tuckwell
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Benoit Tyl
- Translational and Clinical Research, Servier Cardiovascular Center for Therapeutic Innovation, Suresnes, France
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Felix Vaura
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Abirami Veluchamy
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Adriaan A Voors
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Xiaosong Wang
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Peter E Weeke
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Raul Weiss
- Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University Medical Center, Columbus, OH, USA
| | - Harvey D White
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
| | - Kerri L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Heming Xing
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Yifan Yang
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Faiez Zannad
- CHU de Nancy, Inserm and INI-CRCT (F-CRIN), Institut Lorrain du Coeur et des Vaisseaux, Université de Lorraine, Nancy, France
| | - Faye Zhao
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | -
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Jemma B Wilk
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Naveed Sattar
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Steven A Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA.,Heart and Vascular Institute, Henry Ford Hospital, Detroit, MI, USA
| | - Svati Shah
- Duke Molecular Physiology Institute, Durham, NC, USA.,Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA.,Duke Clinical Research Institute, Durham, NC, USA
| | - Michael E Dunn
- Regeneron Pharmaceuticals, Cardiovascular Research, Tarrytown, NY, USA
| | - Quinn S Wells
- Division of Cardiovascular Medicine and the Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University, Nashville, TN, USA
| | - Folkert W Asselbergs
- Health Data Research UK London, University College London, London, UK.,BHF Research Accelerator, University College London, London, UK.,Institute of Cardiovascular Science, University College London, London, UK.,Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Aroon D Hingorani
- BHF Research Accelerator, University College London, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Chim C Lang
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Thomas P Cappola
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Ramachandran S Vasan
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA.,Sections of Cardiology, Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - J Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden.,Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden.,The Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and the Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
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Katsoulis M, Stavola BD, Diaz-Ordaz K, Gomes M, Lai A, Lagiou P, Wannamethee G, Tsilidis K, Lumbers RT, Denaxas S, Banerjee A, Parisinos CA, Batterham R, Patel R, Langenberg C, Hemingway H. Weight Change and the Onset of Cardiovascular Diseases: Emulating Trials Using Electronic Health Records. Epidemiology 2021; 32:744-755. [PMID: 34348396 PMCID: PMC8318567 DOI: 10.1097/ede.0000000000001393] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 06/11/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Cross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD. METHODS We estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45-69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group. RESULTS Among those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (-0.2% to 1.7%)] and the weight gain group [RD = 0.7% (-0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = -1.4% (-2.4% to -0.6%)] but not of stroke. When we assumed that chronic disease occurred 1-3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals. CONCLUSION Among individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.
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Affiliation(s)
- Michail Katsoulis
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
| | - Bianca D. Stavola
- Great Ormond Street Institute of Child Health, UCL, London, United Kingdom
| | - Karla Diaz-Ordaz
- Medical Statistics Department, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Manuel Gomes
- Institute of Epidemiology & Health Care, UCL, London, United Kingdom
| | - Alvina Lai
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
| | - Pagona Lagiou
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Goya Wannamethee
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - R. Thomas Lumbers
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
- Bart’s Heart Centre, St Bartholomew’s Hospital, London, United Kingdom
| | - Spiros Denaxas
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
- The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, UCL, London, United Kingdom
- British Heart Foundation Research Accelerator, UCL, London, United Kingdom
| | - Amitava Banerjee
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
- Amrita Institute of Medical Sciences, Kochi, India
| | - Constantinos A. Parisinos
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
| | - Rachel Batterham
- The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, UCL, London, United Kingdom
- Centre for Obesity Research, UCL, London, United Kingdom
- University College London Hospitals Bariatric Centre for Weight Management and Metabolic Surgery, London, United Kingdom
| | - Riyaz Patel
- Institute of Cardiovascular Science, UCL, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Harry Hemingway
- From the Institute of Health Informatics, University College London (UCL), London, United Kingdom
- Health Data Research UK London, UCL, London, United Kingdom
- The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, UCL, London, United Kingdom
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38
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Dutey-Magni PF, Williams H, Jhass A, Rait G, Lorencatto F, Hemingway H, Hayward A, Shallcross L. COVID-19 infection and attributable mortality in UK care homes: cohort study using active surveillance and electronic records (March-June 2020). Age Ageing 2021; 50:1019-1028. [PMID: 33710281 PMCID: PMC7989651 DOI: 10.1093/ageing/afab060] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND epidemiological data on COVID-19 infection in care homes are scarce. We analysed data from a large provider of long-term care for older people to investigate infection and mortality during the first wave of the pandemic. METHODS cohort study of 179 UK care homes with 9,339 residents and 11,604 staff. We used manager-reported daily tallies to estimate the incidence of suspected and confirmed infection and mortality in staff and residents. Individual-level electronic health records from 8,713 residents were used to model risk factors for confirmed infection, mortality and estimate attributable mortality. RESULTS 2,075/9,339 residents developed COVID-19 symptoms (22.2% [95% confidence interval: 21.4%; 23.1%]), while 951 residents (10.2% [9.6%; 10.8%]) and 585 staff (5.0% [4.7%; 5.5%]) had laboratory-confirmed infections. The incidence of confirmed infection was 152.6 [143.1; 162.6] and 62.3 [57.3; 67.5] per 100,000 person-days in residents and staff, respectively. Sixty-eight percent (121/179) of care homes had at least one COVID-19 infection or COVID-19-related death. Lower staffing ratios and higher occupancy rates were independent risk factors for infection.Out of 607 residents with confirmed infection, 217 died (case fatality rate: 35.7% [31.9%; 39.7%]). Mortality in residents with no direct evidence of infection was twofold higher in care homes with outbreaks versus those without (adjusted hazard ratio: 2.2 [1.8; 2.6]). CONCLUSIONS findings suggest many deaths occurred in people who were infected with COVID-19, but not tested. Higher occupancy and lower staffing levels were independently associated with risks of infection. Protecting staff and residents from infection requires regular testing for COVID-19 and fundamental changes to staffing and care home occupancy.
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Affiliation(s)
- Peter F Dutey-Magni
- Institute of Health Informatics, University College London, NW1 2DA, London, UK
| | | | - Arnoupe Jhass
- Institute of Health Informatics, University College London, NW1 2DA, London, UK
- Primary Care & Population Health, University College London, NW3 2PF, London, UK
| | - Greta Rait
- Primary Care & Population Health, University College London, NW3 2PF, London, UK
- NIHR Biomedical Research Centre, University College London Hospitals, W1T 7DN, London, UK
| | - Fabiana Lorencatto
- Centre for Behaviour Change, University College London, WC1E 7HB, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, NW1 2DA, London, UK
- NIHR Biomedical Research Centre, University College London Hospitals, W1T 7DN, London, UK
- Health Data Research UK, University College London, NW1 2DA, London, UK
| | - Andrew Hayward
- Institute of Epidemiology & Health Care, University College London, WC1E 7HB, London, UK
| | - Laura Shallcross
- Institute of Health Informatics, University College London, NW1 2DA, London, UK
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39
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Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, Gill S, Kotecha D, Freitag DF, Denaxas S, Hemingway H. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021; 19:85. [PMID: 33820530 PMCID: PMC8022365 DOI: 10.1186/s12916-021-01940-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). METHODS For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. RESULTS Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). CONCLUSIONS Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.
- Health Data Research UK, University College London, London, UK.
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK.
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
| | - Suliang Chen
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | | | - R Thomas Lumbers
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Simrat Gill
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dipak Kotecha
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
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40
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Clift AK, Coupland CAC, Keogh RH, Hemingway H, Hippisley-Cox J. COVID-19 Mortality Risk in Down Syndrome: Results From a Cohort Study of 8 Million Adults. Ann Intern Med 2021; 174:572-576. [PMID: 33085509 PMCID: PMC7592804 DOI: 10.7326/m20-4986] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
| | | | - Ruth H Keogh
- London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Harry Hemingway
- University College London, Health Data Research UK, and National Institute for Health Research Biomedical Research Centre, London, United Kingdom
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41
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Uijl A, Vaartjes I, Denaxas S, Hemingway H, Shah A, Cleland J, Grobbee D, Hoes A, Asselbergs FW, Koudstaal S. Temporal trends in heart failure medication prescription in a population-based cohort study. BMJ Open 2021; 11:e043290. [PMID: 33653753 PMCID: PMC7929882 DOI: 10.1136/bmjopen-2020-043290] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE We examined temporal heart failure (HF) prescription patterns in a large representative sample of real-world patients in the UK, using electronic health records (EHR). METHODS From primary and secondary care EHR, we identified 85 732 patients with a HF diagnosis between 2002 and 2015. Almost 50% of patients with HF were women and the median age was 79.1 (IQR 70.2-85.7) years, with age at diagnosis increasing over time. RESULTS We found several trends in pharmacological HF management, including increased beta blocker prescriptions over time (29% in 2002-2005 and 54% in 2013-2015), which was not observed for mineralocorticoid receptor-antagonists (MR-antagonists) (18% in 2002-2005 and 18% in 2013-2015); higher prescription rates of loop diuretics in women and elderly patients together with lower prescription rates of angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers, beta blockers or MR-antagonists in these patients; little change in medication prescription rates occurred after 6 months of HF diagnosis and, finally, patients hospitalised for HF who had no recorded follow-up in primary care had considerably lower prescription rates compared with patients with a HF diagnosis in primary care with or without HF hospitalisation. CONCLUSION In the general population, the use of MR-antagonists for HF remained low and did not change throughout 13 years of follow-up. For most patients, few changes were seen in pharmacological management of HF in the 6 months following diagnosis.
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Affiliation(s)
- Alicia Uijl
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Ilonca Vaartjes
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - S Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- Alan Turing Institute, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
| | - Anoop Shah
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - J Cleland
- Robertson Centre for Biostatistics and Clinical Trials, University of Glasgow, Glasgow, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Diederick Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Arno Hoes
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Stefan Koudstaal
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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42
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Papez V, Moinat M, Payralbe S, Asselbergs FW, Lumbers RT, Hemingway H, Dobson R, Denaxas S. Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure. JAMIA Open 2021; 4:ooab001. [PMID: 34514354 PMCID: PMC8423424 DOI: 10.1093/jamiaopen/ooab001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/16/2020] [Accepted: 01/05/2021] [Indexed: 11/13/2022] Open
Abstract
Objective The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. Materials and Methods Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. Results We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). Conclusion Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.
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Affiliation(s)
- Vaclav Papez
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK
| | | | | | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK.,Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK
| | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK.,Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK.,The Alan Turing Institute, London, UK
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Kuan V, Fraser HC, Hingorani M, Denaxas S, Gonzalez-Izquierdo A, Direk K, Nitsch D, Mathur R, Parisinos CA, Lumbers RT, Sofat R, Wong ICK, Casas JP, Thornton JM, Hemingway H, Partridge L, Hingorani AD. Data-driven identification of ageing-related diseases from electronic health records. Sci Rep 2021; 11:2938. [PMID: 33536532 PMCID: PMC7859412 DOI: 10.1038/s41598-021-82459-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/20/2021] [Indexed: 11/09/2022] Open
Abstract
Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.
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Affiliation(s)
- Valerie Kuan
- Institute of Health Informatics, University College London, London, UK. .,Health Data Research UK London, University College London, London, UK. .,University College London British Heart Foundation Research Accelerator, London, UK.
| | - Helen C Fraser
- Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, University College London, London, UK
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK.,University College London British Heart Foundation Research Accelerator, London, UK.,Alan Turing Institute, London, UK
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK
| | - Kenan Direk
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK
| | - Dorothea Nitsch
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rohini Mathur
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK.,University College London British Heart Foundation Research Accelerator, London, UK.,Barts Heart Centre, St Bartholomew's Hospital, London, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK.,University College London British Heart Foundation Research Accelerator, London, UK
| | - Ian C K Wong
- School of Pharmacy, University College London, London, WC1N 1AX, UK.,Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Juan P Casas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Janet M Thornton
- European Molecular Biology Laboratory - European Bioinformatics Institute EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK London, University College London, London, UK.,University College London British Heart Foundation Research Accelerator, London, UK.,The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, W1T 7DN, UK
| | - Linda Partridge
- Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, University College London, London, UK.,Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Aroon D Hingorani
- Health Data Research UK London, University College London, London, UK.,University College London British Heart Foundation Research Accelerator, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
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Katsoulis M, Pasea L, Lai AG, Dobson RJB, Denaxas S, Hemingway H, Banerjee A. Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study. Public Health 2021; 191:41-47. [PMID: 33497994 PMCID: PMC7832229 DOI: 10.1016/j.puhe.2020.12.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/29/2020] [Accepted: 12/06/2020] [Indexed: 12/28/2022]
Abstract
OBJECTIVES Obesity is a modifiable risk factor for coronavirus disease 2019 (COVID-19)-related mortality. We estimated excess mortality in obesity, both 'direct', through infection, and 'indirect', through changes in health care, and also due to potential increasing obesity during lockdown. STUDY DESIGN The study design of this study is a retrospective cohort study and causal inference methods. METHODS In population-based electronic health records for 1,958,638 individuals in England, we estimated 1-year mortality risk ('direct' and 'indirect' effects) for obese individuals, incorporating (i) pre-COVID-19 risk by age, sex and comorbidities, (ii) population infection rate and (iii) relative impact on mortality (relative risk [RR]: 1.2, 1.5, 2.0 and 3.0). Using causal inference models, we estimated impact of change in body mass index (BMI) and physical activity during 3-month lockdown on 1-year incidence for high-risk conditions (cardiovascular diseases, diabetes, chronic obstructive pulmonary disease and chronic kidney disease), accounting for confounders. RESULTS For severely obese individuals (3.5% at baseline), at 10% population infection rate, we estimated direct impact of 240 and 479 excess deaths in England at RR 1.5 and 2.0, respectively, and indirect effect of 383-767 excess deaths, assuming 40% and 80% will be affected at RR = 1.2. Owing to BMI change during the lockdown, we estimated that 97,755 (5.4%: normal weight to overweight, 5.0%: overweight to obese and 1.3%: obese to severely obese) to 434,104 individuals (15%: normal weight to overweight, 15%: overweight to obese and 6%: obese to severely obese) would be at higher risk for COVID-19 over one year. CONCLUSIONS Prevention of obesity and promotion of physical activity are at least as important as physical isolation of severely obese individuals during the pandemic.
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Affiliation(s)
- M Katsoulis
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - L Pasea
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - A G Lai
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - R J B Dobson
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - S Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - H Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - A Banerjee
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; University College London Hospitals NHS Trust, 235 Euston Road, London, UK; Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
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Katsoulis M, Gomes M, Lai AG, Henry A, Denaxas S, Lagiou P, Nafilyan V, Humberstone B, Banerjee A, Hemingway H, Lumbers RT. Estimating the Effect of Reduced Attendance at Emergency Departments for Suspected Cardiac Conditions on Cardiac Mortality During the COVID-19 Pandemic. Circ Cardiovasc Qual Outcomes 2020; 14:e007085. [PMID: 33342219 PMCID: PMC7819531 DOI: 10.1161/circoutcomes.120.007085] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Michail Katsoulis
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom
| | - Manuel Gomes
- Institute of Epidemiology & Health Care (M.G.), University College London, United Kingdom
| | - Alvina G Lai
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom
| | - Albert Henry
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom
| | - Spiros Denaxas
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,The National Institute for Health Research University College London Hospitals Biomedical Research Centre (S.D., H.H., R.T.L), University College London, United Kingdom.,British Heart Foundation Research Accelerator (S.D.), University College London, United Kingdom.,The Alan Turing Institute, London, United Kingdom (S.D.)
| | - Pagona Lagiou
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Greece (P.L.).,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (P.L.)
| | - Vahe Nafilyan
- Office for National Statistics, Newport, United Kingdom (V.N., B.H.)
| | - Ben Humberstone
- Office for National Statistics, Newport, United Kingdom (V.N., B.H.)
| | - Amitava Banerjee
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Amrita Institute of Medical Sciences, Kochi, India (A.B.)
| | - Harry Hemingway
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom
| | - R Thomas Lumbers
- Institute of Health Informatics (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,Health Data Research UK London (M.K., A.G.L., A.H., S.D., A.B., H.H., R.T.L.), University College London, United Kingdom.,The National Institute for Health Research University College London Hospitals Biomedical Research Centre (S.D., H.H., R.T.L), University College London, United Kingdom.,Bart's Heart Centre, St. Bartholomew's Hospital, London, United Kingdom (R.T.L.)
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Ball S, Banerjee A, Berry C, Boyle JR, Bray B, Bradlow W, Chaudhry A, Crawley R, Danesh J, Denniston A, Falter F, Figueroa JD, Hall C, Hemingway H, Jefferson E, Johnson T, King G, Lee KK, McKean P, Mason S, Mills NL, Pearson E, Pirmohamed M, Poon MTC, Priedon R, Shah A, Sofat R, Sterne JAC, Strachan FE, Sudlow CLM, Szarka Z, Whiteley W, Wyatt M. Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK. Heart 2020; 106:1890-1897. [PMID: 33020224 PMCID: PMC7536637 DOI: 10.1136/heartjnl-2020-317870] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/16/2020] [Accepted: 09/18/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects. METHODS Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. RESULTS Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. CONCLUSIONS Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.
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Affiliation(s)
- Simon Ball
- University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom
- Health Data Research UK Midlands, Birmingham, United Kingdom
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, United Kingdom
- University College London Hospitals NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Colin Berry
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
- Department of Cardiology, Golden Jubilee National Hospital, Clydebank, UK
| | - Jonathan R Boyle
- University of Cambridge, Cambridge, Cambridgeshire, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | | | - William Bradlow
- Department of Cardiology, University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Rikki Crawley
- Barnsley Hospital NHS Foundation Trust, Barnsley, South Yorkshire, UK
| | - John Danesh
- University of Cambridge, Cambridge, Cambridgeshire, UK
- Health Data Research UK Cambridge, Cambridge, UK
| | - Alastair Denniston
- University Hospitals Birmingham NHS Trust, Birmingham, United Kingdom
- Health Data Research UK Midlands, Birmingham, United Kingdom
| | - Florian Falter
- Royal Papworth Hospital NHS Foundation Trust, Cambridge, Cambridgeshire, UK
| | - Jonine D Figueroa
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | | | - Harry Hemingway
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Emily Jefferson
- Population Health and Genomics, University of Dundee, Dundee, UK
- Health Data Research UK Scotland, Edinburgh, United Kingdom
| | - Tom Johnson
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Graham King
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Kuan Ken Lee
- Centre for Cardiovascular Sciences, The University of Edinburgh, Edinburgh, UK
| | - Paul McKean
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Suzanne Mason
- Health Data Research UK North, Sheffield, UK
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Nicholas L Mills
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
- Health Data Research UK Scotland, Edinburgh, United Kingdom
- BHF Centre for Cardiovascular Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ewen Pearson
- Population Health and Genomics, University of Dundee, Dundee, UK
- Health Data Research UK Scotland, Edinburgh, United Kingdom
| | - Munir Pirmohamed
- Health Data Research UK North, Sheffield, UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Michael T C Poon
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Institute of Genomic and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Rouven Priedon
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Anoop Shah
- BHF/University Centre for Cardiovascular Science, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Reecha Sofat
- University College London Hospitals NHS Trust, London, United Kingdom
- Institute of Health Informatics, University College London, London, UK
| | - Jonathan A C Sterne
- Health Data Research UK South West, Population Health Sciences, University of Bristol, Bristol, UK
| | - Fiona E Strachan
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Cathie L M Sudlow
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
- Health Data Research UK Scotland, Edinburgh, United Kingdom
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Zsolt Szarka
- University of Dundee Health Informatics Centre, Dundee, UK
| | - William Whiteley
- The University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK
| | - Michael Wyatt
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
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Parisinos CA, Wilman HR, Thomas EL, Kelly M, Nicholls RC, McGonigle J, Neubauer S, Hingorani AD, Patel RS, Hemingway H, Bell JD, Banerjee R, Yaghootkar H. Corrigendum to: "Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis" [J Hepatol (2020) 241-251]. J Hepatol 2020; 73:1594-1595. [PMID: 32951912 PMCID: PMC8055539 DOI: 10.1016/j.jhep.2020.08.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Affiliation(s)
- Constantinos A. Parisinos
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK,Corresponding authors. Addresses: Institute of Health Informatics, Faculty of Population Health Sciences, University College London, NW12DA. Tel.: +(44)7899786998
| | - Henry R. Wilman
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK,Perspectum Diagnostics Ltd., Oxford, UK
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | | | - Stefan Neubauer
- Perspectum Diagnostics Ltd., Oxford, UK,Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Aroon D. Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Riyaz S. Patel
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Harry Hemingway
- Health Data Research UK London, Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Hanieh Yaghootkar
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK; Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK; Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden.
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Denaxas S, Shah AD, Mateen BA, Kuan V, Quint JK, Fitzpatrick N, Torralbo A, Fatemifar G, Hemingway H. A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems. JAMIA Open 2020; 3:545-556. [PMID: 33619467 PMCID: PMC7717266 DOI: 10.1093/jamiaopen/ooaa047] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/10/2020] [Accepted: 09/14/2020] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVES The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers. MATERIALS AND METHODS We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models. RESULTS We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured. DISCUSSION AND CONCLUSION Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.
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Affiliation(s)
- Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- The Alan Turing Institute, London UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Anoop D Shah
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Bilal A Mateen
- The Alan Turing Institute, London UK
- King’s College Hospital, London, UK
| | - Valerie Kuan
- Health Data Research UK, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
| | - Jennifer K Quint
- Health Data Research UK, University College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Natalie Fitzpatrick
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Ana Torralbo
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
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Lai AG, Pasea L, Banerjee A, Hall G, Denaxas S, Chang WH, Katsoulis M, Williams B, Pillay D, Noursadeghi M, Linch D, Hughes D, Forster MD, Turnbull C, Fitzpatrick NK, Boyd K, Foster GR, Enver T, Nafilyan V, Humberstone B, Neal RD, Cooper M, Jones M, Pritchard-Jones K, Sullivan R, Davie C, Lawler M, Hemingway H. Estimated impact of the COVID-19 pandemic on cancer services and excess 1-year mortality in people with cancer and multimorbidity: near real-time data on cancer care, cancer deaths and a population-based cohort study. BMJ Open 2020; 10:e043828. [PMID: 33203640 PMCID: PMC7674020 DOI: 10.1136/bmjopen-2020-043828] [Citation(s) in RCA: 185] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVES To estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer. METHODS We employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England. RESULTS Declines in urgent referrals (median=-70.4%) and chemotherapy attendances (median=-41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=-44.5%) and chemotherapy attendances (median=-31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity. CONCLUSIONS Dramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.
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Affiliation(s)
- Alvina G Lai
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK
| | - Geoff Hall
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- The Alan Turing Institute, London, UK
| | - Wai Hoong Chang
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK
| | - Bryan Williams
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Deenan Pillay
- Division of Infection and Immunity, University College London, London, UK
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK
| | - David Linch
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
- Department of Hematology, University College London Cancer Institute, London, UK
| | - Derralynn Hughes
- University College London Cancer Institute, London, UK
- Royal Free NHS Foundation Trust, London, UK
| | - Martin D Forster
- University College London Hospitals NHS Trust, London, UK
- University College London Cancer Institute, London, UK
| | - Clare Turnbull
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Natalie K Fitzpatrick
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Kathryn Boyd
- Northern Ireland Cancer Network, Northern Ireland, UK
| | - Graham R Foster
- Barts Liver Centre, Blizard Institute, Queen Mary University of London, London, UK
| | - Tariq Enver
- University College London Cancer Institute, London, UK
| | | | | | - Richard D Neal
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Matt Cooper
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Monica Jones
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Kathy Pritchard-Jones
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- UCLPartners Academic Health Science Partnership, London, UK
- Centre for Cancer Outcomes, University College London Hospitals NHS Foundation Trust, London, UK
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Richard Sullivan
- Conflict and Health Research Group, Institute of Cancer Policy, King's College London, London, UK
| | - Charlie Davie
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- Royal Free NHS Foundation Trust, London, UK
- UCLPartners Academic Health Science Partnership, London, UK
| | - Mark Lawler
- DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NIHR Biomedical Research Centre, London, UK
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Zhang R, Mamza J, Morris T, Godfrey G, Asselbergs F, Denaxas S, Hemingway H, Banerjee A. Lifetime risk of cardiovascular-renal disease in type 2 diabetes: a population-based study in 473399 individuals. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.3313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Lifetime risks of cardiovascular (CV) and renal diseases are high, particularly in type 2 diabetes (T2D), but rarely studied together, and relative disease contributions are unknown. Knowledge of lifetime risk of cardiovascular-renal disease (CVRD) will better reflect disease burden in T2D.
Purpose
To investigate the lifetime risks (LTRs) of composite and individual components of major adverse reno-cardiovascular events, MARCE in T2D patients.
Method
In a population-based cohort study using national electronic health records, we studied 473399 individuals aged 45–99 years with T2D in England 2007–2018. The LTR of composite and individual components of MARCE (including CV death and CVRD: heart failure, HF; chronic kidney disease stage 3 and above, CKD; myocardial infarction, MI; stroke or peripheral artery disease, PAD) were estimated. LTRs by baseline CVRD comorbidity status were compared with individuals free from CVRD at baseline, accounting for the competing risk of death.
Results
Among T2D patients aged ≥45 years, the LTR of MARCE was 80% for individuals free from CVRD at baseline. LTR of MARCE was 97%, 93%, 98%, 89% and 91% for individuals with specific CVRD comorbidities for HF, CKD, MI, stroke and PAD, respectively at baseline. Within the CVRD-free cohort, LTR of CKD was highest at 54%, followed by CV death (41%), HF (29%), stroke (20%), MI (19%) and PAD (9%). Compared to CVRD-free, HF, MI and CKD at baseline were associated with the highest LTR of MARCE and its component diseases (Table).
Conclusion
The lifetime risk of CV disease and CKD in T2D patients is estimated to be over 60% and 50% respectively (1–3). When considered together, the LTR of MARCE is 80% in CVRD-free T2D patients, while nearly all those with T2D and HF will develop MARCE over their lifetime. Of the individual components of MARCE, LTR of CKD and HF were the highest among CVRD-free T2D patients. Preventive measures in T2D patients should be a priority in clinical practice to mitigate the burden of these complications.
Funding Acknowledgement
Type of funding source: Private company. Main funding source(s): AstraZeneca
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Affiliation(s)
- R Zhang
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, United Kingdom
| | - J Mamza
- AstraZeneca, Luton, United Kingdom
| | - T Morris
- AstraZeneca, Luton, United Kingdom
| | | | - F Asselbergs
- University College London, London, United Kingdom
| | - S Denaxas
- University College London, London, United Kingdom
| | - H Hemingway
- University College London, Health Data Research UK, London, United Kingdom
| | - A Banerjee
- University College London Hospitals, Department of Cardiology, London, United Kingdom
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