Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study

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.

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK

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.

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems

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.

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

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.

Lifetime risk of cardiovascular-renal disease in type 2 diabetes: a population-based study in 473399 individuals

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

92 aetiologic factors for heart failure: prevalence, co-occurrence, prognosis and potential for prevention in 170,885 incident HF cases

Background

ESC and AHA guidelines identify 92 putative aetiological factors for heart failure, but primary prevention strategies for heart failure (HF) have had limited success. There are no previous studies systematically evaluating the potential for prevention among such a wide range of clinically manifest aetiologic factors.

Objective

To estimate for 92 (putative) aetiologic factors their prevalence, co-occurrence, preventability and impact on HF prognosis in incident HF.

Methods

We identified 170885 individuals aged ≥30 years with incident HF from 1997–2017 using linked primary and secondary care electronic health records (EHR) in the UK (CALIBER). For each of the 92 factors we developed EHR phenotypes using ICD-10 diagnosis, Read, procedure and medication codes (total = 5961 codes). We conducted a literature review of trials of primary HF prevention across 92 factors.

Results

35.6% of individuals with HF had ≥4 aetiologic factors recorded in the preceding 5 years. Of all new HF cases 71.5% had ≥1 of the 7 aetiologic factors with trial evidence of effective preventive therapy for HF; 12.9% had ≥1 aetiologic factor with effective preventive therapy for CVD and 15.6% had either no aetiological factor, or an aetiological factor with no evidence of preventive potential. 88/91 factors were represented. The commonest factors in the 5 years prior to HF diagnosis were hypertension (48.7%), smoking (46.6%), reduced physical activity (39.6%), stable angina (35.6%), obesity (29.4%), atrial arrhythmias (17.3%), unstable angina (16.9%), cancer (16.6%), myocardial infarction (15.9%), diabetes (15.2%), alcohol (14.9%) and severe anaemia (14.3%). Mortality at 1 year varied across all 91 factors (lowest: pregnancy-related hormonal disorder – 4.2%; highest: phaeochromocytoma – 73.7%).

Conclusion

We provide a systematic map of primary preventive opportunities for heart failure, demonstrate the high burden of co-occurring aetiologic factors and highlight need for trials tackling multiple aetiological factors.

Aetiologic factors of heart failure

Funding Acknowledgement

Type of funding source: Public grant(s) – EU funding. Main funding source(s): Innovative Medicines Initiative-2 Joint Undertaking

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

OBJECTIVE

To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

DESIGN

Population based cohort study.

SETTING AND PARTICIPANTS

QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

MAIN OUTCOME MEASURES

The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

RESULTS

4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R²); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

CONCLUSION

The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

Invasive versus non-invasive management of older patients with non-ST elevation myocardial infarction (SENIOR-NSTEMI): a cohort study based on routine clinical data

BACKGROUND

Previous trials suggest lower long-term risk of mortality after invasive rather than non-invasive management of patients with non-ST elevation myocardial infarction (NSTEMI), but the trials excluded very elderly patients. We aimed to estimate the effect of invasive versus non-invasive management within 3 days of peak troponin concentration on the survival of patients aged 80 years or older with NSTEMI.

METHODS

Routine clinical data for this study were obtained from five collaborating hospitals hosting NIHR Biomedical Research Centres in the UK (all tertiary centres with emergency departments). Eligible patients were 80 years old or older when they underwent troponin measurements and were diagnosed with NSTEMI between 2010 (2008 for University College Hospital) and 2017. Propensity scores (patients' estimated probability of receiving invasive management) based on pretreatment variables were derived using logistic regression; patients with high probabilities of non-invasive or invasive management were excluded. Patients who died within 3 days of peak troponin concentration without receiving invasive management were assigned to the invasive or non-invasive management groups based on their propensity scores, to mitigate immortal time bias. We estimated mortality hazard ratios comparing invasive with non-invasive management, and compared the rate of hospital admissions for heart failure.

FINDINGS

Of the 1976 patients with NSTEMI, 101 died within 3 days of their peak troponin concentration and 375 were excluded because of extreme propensity scores. The remaining 1500 patients had a median age of 86 (IQR 82-89) years of whom (845 [56%] received non-invasive management. During median follow-up of 3·0 (IQR 1·2-4·8) years, 613 (41%) patients died. The adjusted cumulative 5-year mortality was 36% in the invasive management group and 55% in the non-invasive management group (adjusted hazard ratio 0·68, 95% CI 0·55-0·84). Invasive management was associated with lower incidence of hospital admissions for heart failure (adjusted rate ratio compared with non-invasive management 0·67, 95% CI 0·48-0·93).

INTERPRETATION

The survival advantage of invasive compared with non-invasive management appears to extend to patients with NSTEMI who are aged 80 years or older.

FUNDING

NIHR Imperial Biomedical Research Centre, as part of the NIHR Health Informatics Collaborative.

Clinical academic research in the time of Corona: A simulation study in England and a call for action

OBJECTIVES

We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research.

DESIGN

A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics).

SETTING

Clinical academics in primary and secondary care in England.

PARTICIPANTS

Equivalent of 3200 full-time clinical academics in England.

INTERVENTIONS

Four policy approaches to COVID-19 with differing population infection rates: "Italy model" (6%), "mitigation" (10%), "relaxed mitigation" (40%) and "do-nothing" (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively.

MAIN OUTCOME MEASURES

Number of full-time clinical academics available to conduct clinical research during the pandemic in England.

RESULTS

In the "Italy model", "mitigation", "relaxed mitigation" and "do-nothing" scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively-with no clinical academics at all for 37 days in the "do-nothing" scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively.

CONCLUSIONS

Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.

Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

BACKGROUND & AIMS

MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases.

METHODS

First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures.

RESULTS

We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10-8). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective.

CONCLUSION

The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals.

LAY SUMMARY

We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.

Healthcare resource utilisation and mortality outcomes in international migrants to the UK: analysis protocol for a linked population-based cohort study using Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics (HES) and the Office for National Statistics (ONS)

An estimated 14.2% (9.34 million people) of people living in the UK in 2019 were international migrants. Despite this, there are no large-scale national studies of their healthcare resource utilisation and little is known about how migrants access and use healthcare services. One ongoing study of migration health in the UK, the Million Migrants study, links electronic health records (EHRs) from hospital-based data, national death records and Public Health England migrant and refugee data. However, the Million Migrants study cannot provide a complete picture of migration health resource utilisation as it lacks data on migrants from Europe and utilisation of primary care for all international migrants. Our study seeks to address this limitation by using primary care EHR data linked to hospital-based EHRs and national death records.  Our study is split into a feasibility study and a main study. The feasibility study will assess the validity of a migration phenotype, a transparent reproducible algorithm using clinical terminology codes to determine migration status in Clinical Practice Research Datalink (CPRD), the largest UK primary care EHR. If the migration phenotype is found to be valid, the main study will involve using the phenotype in the linked dataset to describe primary care and hospital-based healthcare resource utilisation and mortality in migrants compared to non-migrants. All outcomes will be explored according to sub-conditions identified as research priorities through patient and public involvement, including preventable causes of inpatient admission, sexual and reproductive health conditions/interventions and mental health conditions. The results will generate evidence to inform policies that aim to improve migration health and universal health coverage.

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

BACKGROUND

The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.

METHODS

In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation.

FINDINGS

We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0.

INTERPRETATION

We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality.

FUNDING

National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

A national initiative in data science for health: an evaluation of the UK Farr Institute

OBJECTIVE

To evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.

DESIGN AND OUTCOME MEASURES

We evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.

SETTING

Four UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.

RESULTS

1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.

CONCLUSION

The Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Prognostic significance of troponin level in 3121 patients presenting with atrial fibrillation (The NIHR Health Informatics Collaborative TROP-AF study)

Background Patients presenting with atrial fibrillation (AF) often undergo a blood test to measure troponin, but interpretation of the result is impeded by uncertainty about its clinical importance. We investigated the relationship between troponin level, coronary angiography, and all-cause mortality in real-world patients presenting with AF. Methods and Results We used National Institute of Health Research Health Informatics Collaborative data to identify patients admitted between 2010 and 2017 at 5 tertiary centers in the United Kingdom with a primary diagnosis of AF. Peak troponin results were scaled as multiples of the upper limit of normal. A total of 3121 patients were included in the analysis. Over a median follow-up of 1462 (interquartile range, 929-1975) days, there were 586 deaths (18.8%). The adjusted hazard ratio for mortality associated with a positive troponin (value above upper limit of normal) was 1.20 (95% CI, 1.01-1.43; P<0.05). Higher troponin levels were associated with higher risk of mortality, reaching a maximum hazard ratio of 2.6 (95% CI, 1.9-3.4) at ≈250 multiples of the upper limit of normal. There was an exponential relationship between higher troponin levels and increased odds of coronary angiography. The mortality risk was 36% lower in patients undergoing coronary angiography than in those who did not (adjusted hazard ratio, 0.61; 95% CI, 0.42-0.89; P=0.01). Conclusions Increased troponin was associated with increased risk of mortality in patients presenting with AF. The lower hazard ratio in patients undergoing invasive management raises the possibility that the clinical importance of troponin release in AF may be mediated by coronary artery disease, which may be responsive to revascularization.

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

Statistics on mortality following acute myocardial infarction in 842 897 Europeans

AIMS

To compare ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) mortality between Sweden and the UK, adjusting for background population rates of expected death, case mix, and treatments.

METHODS AND RESULTS

National data were collected from hospitals in Sweden [n = 73 hospitals, 180 368 patients, Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART)] and the UK [n = 247, 662 529 patients, Myocardial Ischaemia National Audit Project (MINAP)] between 2003 and 2013. There were lower rates of revascularization [STEMI (43.8% vs. 74.9%); NSTEMI (27.5% vs. 43.6%)] and pharmacotherapies at time of hospital discharge including [aspirin (82.9% vs. 90.2%) and (79.9% vs. 88.0%), β-blockers (73.4% vs. 86.4%) and (65.3% vs. 85.1%)] in the UK compared with Sweden, respectively. Standardized net probability of death (NPD) between admission and 1 month was higher in the UK for STEMI [8.0 (95% confidence interval 7.4-8.5) vs. 6.7 (6.5-6.9)] and NSTEMI [6.8 (6.4-7.2) vs. 4.9 (4.7-5.0)]. Between 6 months and 1 year and more than 1 year, NPD remained higher in the UK for NSTEMI [2.9 (2.5-3.3) vs. 2.3 (2.2-2.5)] and [21.4 (20.0-22.8) vs. 18.3 (17.6-19.0)], but was similar for STEMI [0.7 (0.4-1.0) vs. 0.9 (0.7-1.0)] and [8.4 (6.7-10.1) vs. 8.3 (7.5-9.1)].

CONCLUSION

Short-term mortality following STEMI and NSTEMI was higher in the UK compared with Sweden. Mid- and longer-term mortality remained higher in the UK for NSTEMI but was similar for STEMI. Differences in mortality may be due to differential use of guideline-indicated treatments.

Metformin use and cardiovascular outcomes after acute myocardial infarction in patients with type 2 diabetes: a cohort study

Background

The use of metformin after acute myocardial infarction (AMI) has been associated with reduced mortality in people with type 2 diabetes mellitus (T2DM). However, it is not known if it is acutely cardioprotective in patients taking metformin at the time of AMI. We compared patient outcomes according to metformin status at the time of admission for fatal and non-fatal AMI in a large cohort of patients in England.

Methods

This study used linked data from primary care, hospital admissions and death registry from 4.7 million inhabitants in England, as part of the CALIBER resource. The primary endpoint was a composite of acute myocardial infarction requiring hospitalisation, stroke and cardiovascular death. The secondary endpoints were heart failure (HF) hospitalisation and all-cause mortality.

Results

4,030 patients with T2DM and incident AMI recorded between January 1998 and October 2010 were included. At AMI admission, 63.9% of patients were receiving metformin and 36.1% another oral hypoglycaemic drug. Median follow-up was 343 (IQR: 1–1436) days. Adjusted analyses showed an increased hazard of the composite endpoint in metformin users compared to non-users (HR 1.09 [1.01–1.19]), but not of the secondary endpoints. The higher risk of the composite endpoint in metformin users was only observed in people taking metformin at AMI admission, whereas metformin use post-AMI was associated with a reduction in risk of all-cause mortality (0.76 [0.62–0.93], P = 0.009).

Conclusions

Our study suggests that metformin use at the time of first AMI is associated with increased risk of cardiovascular disease and death in patients with T2DM, while its use post-AMI might be beneficial. Further investigation in well-designed randomised controlled trials is indicated, especially in view of emerging evidence of cardioprotection from sodium-glucose co-transporter-2 (SGLT2) inhibitors.

Guideline-indicated treatments and diagnostics, GRACE risk score, and survival for non-ST elevation myocardial infarction

Aims

To investigate whether improved survival from non-ST-elevation myocardial infarction (NSTEMI), according to GRACE risk score, was associated with guideline-indicated treatments and diagnostics, and persisted after hospital discharge.

Methods and results

National cohort study (n = 389 507 patients, n = 232 hospitals, MINAP registry), 2003–2013. The primary outcome was adjusted all-cause survival estimated using flexible parametric survival modelling with time-varying covariates. Optimal care was defined as the receipt of all eligible treatments and was inversely related to risk status (defined by the GRACE risk score): 25.6% in low, 18.6% in intermediate, and 11.5% in high-risk NSTEMI. At 30 days, the use of optimal care was associated with improved survival among high [adjusted hazard ratio (aHR) −0.66 95% confidence interval (CI) 0.53–0.86, difference in absolute mortality rate (AMR) per 100 patients (AMR/100–0.19 95% CI −0.29 to −0.08)], and intermediate (aHR = 0.74, 95% CI 0.62–0.92; AMR/100 = −0.15, 95% CI −0.23 to −0.08) risk NSTEMI. At the end of follow-up (8.4 years, median 2.3 years), the significant association between the use of all eligible guideline-indicated treatments and improved survival remained only for high-risk NSTEMI (aHR = 0.66, 95% CI 0.50–0.96; AMR/100 = −0.03, 95% CI −0.06 to −0.01). For low-risk NSTEMI, there was no association between the use of optimal care and improved survival at 30 days (aHR = 0.92, 95% CI 0.69–1.38) and at 8.4 years (aHR = 0.71, 95% CI 0.39–3.74).

Conclusion

Optimal use of guideline-indicated care for NSTEMI was associated with greater survival gains with increasing GRACE risk, but its use decreased with increasing GRACE risk.

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis

AIMS

To evaluate population-based electronic health record (EHR) definitions of atrial fibrillation (AF) and valvular heart disease (VHD) subtypes, time trends in prevalence and prognosis.

METHODS AND RESULTS

A total of 76 019 individuals with AF were identified in England in 1998-2010 in the CALIBER resource, linking primary and secondary care EHR. An algorithm was created, implemented, and refined to identify 18 VHD subtypes using 406 diagnosis, procedure, and prescription codes. Cox models were used to investigate associations with a composite endpoint of incident stroke (ischaemic, haemorrhagic, and unspecified), systemic embolism (SSE), and all-cause mortality. Among individuals with AF, the prevalence of AF with concomitant VHD increased from 11.4% (527/4613) in 1998 to 17.6% (7014/39 868) in 2010 and also in individuals aged over 65 years. Those with mechanical valves, mitral stenosis (MS), or aortic stenosis had highest risk of clinical events compared to AF patients with no VHD, in relative [hazard ratio (95% confidence interval): 1.13 (1.02-1.24), 1.20 (1.05-1.36), and 1.27 (1.19-1.37), respectively] and absolute (excess risk: 2.04, 4.20, and 6.37 per 100 person-years, respectively) terms. Of the 95.2% of individuals with indication for warfarin (men and women with CHA2DS2-VASc ≥1 and ≥2, respectively), only 21.8% had a prescription 90 days prior to the study.

CONCLUSION

Prevalence of VHD among individuals with AF increased from 1998 to 2010. Atrial fibrillation associated with aortic stenosis, MS, or mechanical valves (compared to AF without VHD) was associated with an excess absolute risk of stroke, SSE, and mortality, but anticoagulation was underused in the pre-direct oral anticoagulant (DOAC) era, highlighting need for urgent clarity regarding DOACs in AF and concomitant VHD.

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER

OBJECTIVE

Electronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research.

MATERIALS AND METHODS

We implemented a rule-based phenotyping framework, with up to 6 approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases-Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DM+D prescription codes.

RESULTS

Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications.

CONCLUSIONS

We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research.