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Rout A, Duhan S, Umer M, Li M, Kalra D. Atherosclerotic cardiovascular disease risk prediction: current state-of-the-art. Heart 2024; 110:1005-1014. [PMID: 37918900 DOI: 10.1136/heartjnl-2023-322928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2023] Open
Affiliation(s)
- Amit Rout
- Cardiology, University of Louisville, Louisville, Kentucky, USA
| | - Sanchit Duhan
- Cardiology, Sinai Health System, Baltimore, Maryland, USA
| | - Muhammad Umer
- Cardiology, University of Louisville, Louisville, Kentucky, USA
| | - Miranda Li
- Cardiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Dinesh Kalra
- Cardiology, University of Louisville, Louisville, Kentucky, USA
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Natale F, Franzese R, Marotta L, Mollo N, Solimene A, Luisi E, Gentile C, Loffredo FS, Golino P, Cimmino G. Evolving Concepts of the SCORE System: Subtracting Cholesterol from Risk Estimation: A Way for a Healthy Longevity? Life (Basel) 2024; 14:679. [PMID: 38929662 PMCID: PMC11204887 DOI: 10.3390/life14060679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
The role of cholesterol, mainly low-density lipoproteins (LDL-C), as a causal risk factor for atherosclerotic cardiovascular disease (ASCVD) is now established and accepted by the international scientific community. Based on this evidence, the European and American guidelines recommend early risk stratification and "rapid" achievement of the suggested target according to the risk estimation to reduce the number of major cardiovascular events. Prolonged exposure over the years to high levels of LDL-C is one of the determining factors in the development and progression of atherosclerotic plaque, on which the action of conventional risk factors (cigarette smoking, excess weight, sedentary lifestyle, arterial hypertension, diabetes mellitus) as well as non-conventional risk factors (gut microbiota, hyperuricemia, inflammation), alone or in combination, favors the destabilization of the atherosclerotic lesion with rupture/fissuration/ulceration and consequent formation of intravascular thrombosis, which leads to the acute clinical manifestations of acute coronary syndromes. In the current clinical practice, there is a growing number of cases that, although extremely common, are emblematic of the concept of long-term exposure to the risk factor (LDL hypercholesterolemia), which, not adequately controlled and in combination with other risk factors, has favored the onset of major cardiovascular events. The triple concept of "go lower, start earlier and keep longer!" should be applied in current clinical practice at any level of prevention. In the present manuscript, we will review the current evidence and documents supporting the causal role of LDL-C in determining ASCVD and whether it is time to remove it from any score.
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Affiliation(s)
- Francesco Natale
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
| | - Rosa Franzese
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Luigi Marotta
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Noemi Mollo
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Achille Solimene
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Ettore Luisi
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Carmine Gentile
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Francesco S. Loffredo
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Paolo Golino
- Vanvitelli Cardiology Unit, Monaldi Hospital, 80131 Naples, Italy; (F.N.); (R.F.); (L.M.); (N.M.); (A.S.); (E.L.); (C.G.); (F.S.L.); (P.G.)
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
| | - Giovanni Cimmino
- Department of Translational Medical Sciences, Section of Cardiology, University of Campania Luigi Vanvitelli, 80131 Naples, Italy
- Cardiology Unit, AOU Luigi Vanvitelli, 80138 Naples, Italy
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Ho L, Pugh C, Seth S, Arakelyan S, Lone NI, Lyall MJ, Anand A, Fleuriot JD, Galdi P, Guthrie B. Performance of models for predicting 1-year to 3-year mortality in older adults: a systematic review of externally validated models. THE LANCET. HEALTHY LONGEVITY 2024; 5:e227-e235. [PMID: 38330982 DOI: 10.1016/s2666-7568(23)00264-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 02/10/2024] Open
Abstract
Mortality prediction models support identifying older adults with short life expectancy for whom clinical care might need modifications. We systematically reviewed external validations of mortality prediction models in older adults (ie, aged 65 years and older) with up to 3 years of follow-up. In March, 2023, we conducted a literature search resulting in 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 50 (68%) of 74 validations not reporting calibration. Morbidities (most commonly cardiovascular diseases) were used as predictors by 45 (70%) of 64 of models. For 1-year prediction, 31 (67%) of 46 models had acceptable discrimination, but only one had excellent performance. Models with more than 20 predictors were more likely to have acceptable discrimination (risk ratio [RR] vs <10 predictors 1·68, 95% CI 1·06-2·66), as were models including sex (RR 1·75, 95% CI 1·12-2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95% CI 1·12-3·07). Development and validation of better-performing mortality prediction models in older people are needed.
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Affiliation(s)
- Leonard Ho
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Carys Pugh
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Sohan Seth
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK; School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Stella Arakelyan
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nazir I Lone
- Royal Infirmary of Edinburgh, National Health Service Lothian, Edinburgh, UK; Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Marcus J Lyall
- Royal Infirmary of Edinburgh, National Health Service Lothian, Edinburgh, UK
| | - Atul Anand
- Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jacques D Fleuriot
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK; School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Paola Galdi
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
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Guthrie B, Rogers G, Livingstone S, Morales DR, Donnan P, Davis S, Youn JH, Hainsworth R, Thompson A, Payne K. The implications of competing risks and direct treatment disutility in cardiovascular disease and osteoporotic fracture: risk prediction and cost effectiveness analysis. HEALTH AND SOCIAL CARE DELIVERY RESEARCH 2024; 12:1-275. [PMID: 38420962 DOI: 10.3310/kltr7714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Background Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates. Objectives Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments. Design, participants, main outcome measures, data sources Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25-84 years for cardiovascular disease and aged 30-99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making). Results CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell's c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85-99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best-worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk. Limitations Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated. Conclusions Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making. Study registration This study is registered as PROSPERO CRD42021249959. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
- Bruce Guthrie
- Advanced Care Research Centre, Centre for Population Health Sciences, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Gabriel Rogers
- Manchester Centre for Health Economics, The University of Manchester, Manchester, UK
| | - Shona Livingstone
- Population Health and Genomics Division, University of Dundee, Dundee, UK
| | - Daniel R Morales
- Population Health and Genomics Division, University of Dundee, Dundee, UK
| | - Peter Donnan
- Population Health and Genomics Division, University of Dundee, Dundee, UK
| | - Sarah Davis
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
| | | | - Rob Hainsworth
- Manchester Centre for Health Economics, The University of Manchester, Manchester, UK
| | - Alexander Thompson
- Manchester Centre for Health Economics, The University of Manchester, Manchester, UK
| | - Katherine Payne
- Manchester Centre for Health Economics, The University of Manchester, Manchester, UK
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Jin D, Trichia E, Islam N, Lewington S, Lacey B. Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank. J Epidemiol Community Health 2023; 77:802-808. [PMID: 37699667 DOI: 10.1136/jech-2023-220801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/25/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND Metabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as compared with established atherosclerotic cardiovascular disease (CVD) risk scores. METHODS UK Biobank is a prospective study of 0.5 million participants, aged 40-69 at recruitment. Analyses were restricted to 74 780 participants with metabolic profiling (measured using nuclear magnetic resonance) and without CVD at baseline. Cox regression was used to compare model performance before and after addition of metabolites to QRISK3 (an established CVD risk score used in primary care in England); analyses derived three models, with metabolites selected by association significance or by employing two different machine learning approaches. RESULTS We identified 5097 incident CVD events within the 10-year follow-up. Harrell's C-index of QRISK3 was 0.750 (95% CI 0.739 to 0.763) for women and 0.706 (95% CI 0.696 to 0.716) for men. Adding selected metabolites did not significantly improve measures of discrimination in women (Harrell's C-index of three models are 0.759 (0.747 to 0.772), 0.759 (0.746 to 0.770) and 0.759 (0.748 to 0.771), respectively) or men (0.710 (0.701 to 0.720), 0.710 (0.700 to 0.719) and 0.710 (0.701 to 0.719), respectively), and neither did it improve reclassification or calibration. CONCLUSION This large-scale study applied both conventional and machine learning approaches to assess the potential benefit of metabolic profiling to well-established CVD risk scores. However, there was no evidence that metabolic profiling improved CVD risk prediction in this population.
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Affiliation(s)
- Danyao Jin
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Eirini Trichia
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | - Nazrul Islam
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Sarah Lewington
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
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Boos CJ, Haling U, Schofield S, Cullinan P, Bull AMJ, Fear NT, Bennett AN. Relationship between combat-related traumatic injury and its severity to predicted cardiovascular disease risk: ADVANCE cohort study. BMC Cardiovasc Disord 2023; 23:581. [PMID: 38012542 PMCID: PMC10680223 DOI: 10.1186/s12872-023-03605-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND This study investigated the relationship between combat-related traumatic injury (CRTI) and its severity and predicted cardiovascular disease (CVD) risk. MATERIAL AND METHODS This was an analysis of comparative 10-year predicted CVD risk (myocardial infarction, stroke or CVD-death) using the QRISK®3 scoring-system among adults recruited into the Armed Services Trauma Rehabilitation Outcome (ADVANCE) cohort study. Participants with CRTI were compared to uninjured servicemen frequency-matched by age, sex, rank, deployment (Afghanistan 2003-2014) and role. Injury severity was quantified using the New Injury Severity Score (NISS). RESULTS One thousand one hundred forty four adult combat veterans were recruited, consisting of 579 injured (161 amputees) and 565 uninjured men of similar age ethnicity and time from deployment/injury. Significant mental illness (8.5% vs 4.4%; p = 0.006) and erectile dysfunction (11.6% vs 5.8%; p < 0.001) was more common, body mass index (28.1 ± 3.9 vs 27.4 ± 3.4 kg/m2; p = 0.001) higher and systolic blood pressure variability (median [IQR]) (1.7 [1.2-3.0] vs 2.1 [1.2-3.5] mmHg; p = 0.008) lower among the injured versus uninjured respectively. The relative risk (RR) of predicted CVD (versus the population expected risk) was higher (RR:1.67 [IQR 1.16-2.48]) among the injured amputees versus the injured non-amputees (RR:1.60 [1.13-2.43]) and uninjured groups (RR:1.52 [1.12-2.34]; overall p = 0.015). After adjustment for confounders CRTI, worsening injury severity (higher NISS, blast and traumatic amputation) were independently associated with QRISK®3 scores. CONCLUSION CRTI and its worsening severity were independently associated with increased predicted 10-year CVD risk.
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Affiliation(s)
- Christopher J Boos
- Academic Department of Military Rehabilitation, Defence Medical Rehabilitation Centre, Stanford Hall Estate, Near Loughborough, LE12 5QW, Nottinghamshire, UK.
- The Academic Department of Military Mental Health, King's College London, London, SE5 9RJ, UK.
- Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, BH1 3LT, UK.
- Department of Cardiology, University Hospitals Dorset, Poole Hospital, Longfleet Rd, Poole, BH15 2JB, Dorset, UK.
| | - Usamah Haling
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, SW3 6LR, UK
| | - Susie Schofield
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, SW3 6LR, UK
| | - Paul Cullinan
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, SW3 6LR, UK
| | - Anthony M J Bull
- Centre for Blast Injury Studies, Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Nicola T Fear
- The Academic Department of Military Mental Health, King's College London, London, SE5 9RJ, UK
| | - Alexander N Bennett
- Academic Department of Military Rehabilitation, Defence Medical Rehabilitation Centre, Stanford Hall Estate, Near Loughborough, LE12 5QW, Nottinghamshire, UK
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, SW3 6LR, UK
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Parsons RE, Liu X, Collister JA, Clifton DA, Cairns BJ, Clifton L. Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank. Heart 2023; 109:1690-1697. [PMID: 37423742 PMCID: PMC10646868 DOI: 10.1136/heartjnl-2022-321231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVE To externally evaluate the performance of QRISK3 for predicting 10 year risk of cardiovascular disease (CVD) in the UK Biobank cohort. METHODS We used data from the UK Biobank, a large-scale prospective cohort study of 403 370 participants aged 40-69 years recruited between 2006 and 2010 in the UK. We included participants with no previous history of CVD or statin treatment and defined the outcome to be the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital inpatient records and death registrations. RESULTS Our study population included 233 233 women and 170 137 men, with 9295 and 13 028 incident CVD events, respectively. Overall, QRISK3 had moderate discrimination for UK Biobank participants (Harrell's C-statistic 0.722 in women and 0.697 in men) and discrimination declined by age (<0.62 in all participants aged 65 years or older). QRISK3 systematically overpredicted CVD risk in UK Biobank, particularly in older participants, by as much as 20%. CONCLUSIONS QRISK3 had moderate overall discrimination in UK Biobank, which was best in younger participants. The observed CVD risk for UK Biobank participants was lower than that predicted by QRISK3, particularly for older participants. It may be necessary to recalibrate QRISK3 or use an alternate model in studies that require accurate CVD risk prediction in UK Biobank.
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Affiliation(s)
- Ruth E Parsons
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaonan Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - David A Clifton
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Benjamin J Cairns
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Zhou J, Chen C, Wang J, Liu S, Li X, Wei Y, Ye L, Ye J, Kraus VB, Lv Y, Shi X. Development and Validation of a Lifespan Prediction Model in Chinese Adults Aged 65 Years or Older. J Am Med Dir Assoc 2023; 24:1068-1073.e6. [PMID: 36965505 PMCID: PMC10335838 DOI: 10.1016/j.jamda.2023.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/01/2023] [Accepted: 02/15/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVES Previous studies investigated factors associated with mortality. Nevertheless, evidence is limited regarding the determinants of lifespan. We aimed to develop and validate a lifespan prediction model based on the most important predictors. DESIGN A prospective cohort study. SETTING AND PARTICIPANTS A total of 23,892 community-living adults aged 65 years or older with confirmed death records between 1998 and 2018 from 23 provinces in China. METHODS Information including demographic characteristics, lifestyle, functional health, and prevalence of diseases was collected. The risk prediction model was generated using multivariate linear regression, incorporating the most important predictors identified by the Lasso selection method. We used 1000 bootstrap resampling for the internal validation. The model performance was assessed by adjusted R2, root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficient (ICC). RESULTS Twenty-one predictors were included in the final lifespan prediction model. Older adults with longer lifespans were characterized by older age at baseline, female, minority race, living in rural areas, married, with healthier lifestyles and more leisure engagement, better functional status, and absence of diseases. The predicted lifespans were highly consistent with observed lifespans, with an adjusted R2 of 0.893. RMSE was 2.86 (95% CI 2.84-2.88) and MAE was 2.18 (95% CI 2.16-2.20) years. The ICC between observed and predicted lifespans was 0.971 (95% CI 0.971-0.971). CONCLUSIONS AND IMPLICATIONS The lifespan prediction model was validated with good performance, the web-based prediction tool can be easily applied in practical use as it relies on all easily accessible variables.
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Affiliation(s)
- Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Sixin Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaming Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Virginia Byers Kraus
- Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Livingstone S, Morales DR, Fleuriot J, Donnan PT, Guthrie B. External validation of the QLifetime cardiovascular risk prediction tool: population cohort study. BMC Cardiovasc Disord 2023; 23:194. [PMID: 37061672 PMCID: PMC10105395 DOI: 10.1186/s12872-023-03209-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/28/2023] [Indexed: 04/17/2023] Open
Abstract
BACKGROUND Prediction of lifetime cardiovascular disease (CVD) risk is recommended in many clinical guidelines, but lifetime risk models are rarely externally validated. The aim of this study was to externally validate the QRiskLifetime incident CVD risk prediction tool. METHODS Independent external validation of QRiskLifetime using Clinical Practice Research Datalink data, examining discrimination and calibration in the whole population and stratified by age, and reclassification compared to QRISK3. Since lifetime CVD risk is unobservable, performance was evaluated at 10-years' follow-up, and lifetime performance inferred in terms of performance for in the different age-groups from which lifetime predictions are derived. RESULTS One million, two hundreds sixty thousand and three hundreds twenty nine women and 1,223,265 men were included in the analysis. Discrimination was excellent in the whole population (Harrell's-C = 0.844 in women, 0.808 in men), but moderate to poor stratified by age-group (Harrell's C in people aged 30-44 0.714 for both men and women, in people aged 75-84 0.578 in women and 0.556 in men). Ten-year CVD risk was under-predicted in the whole population, and in all age-groups except women aged 45-64, with worse under-prediction in older age-groups. Compared to those at highest QRISK3 estimated 10-year risk, those with highest lifetime risk were younger (mean age: women 50.5 vs. 71.3 years; men 46.3 vs. 63.8 years) and had lower systolic blood pressure and prevalence of treated hypertension, but had more family history of premature CVD, and were more commonly minority ethnic. Over 10-years, the estimated number needed to treat (NNT) with a statin to prevent one CVD event in people with QRISK3 ≥ 10% was 34 in women and 37 in men, compared to 99 and 100 for those at highest lifetime risk. CONCLUSIONS QRiskLifetime underpredicts 10-year CVD risk in nearly all age-groups, so is likely to also underpredict lifetime risk. Treatment based on lifetime risk has considerably lower medium-term benefit than treatment based on 10-year risk.
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Affiliation(s)
- Shona Livingstone
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | | | - Peter T Donnan
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Old Medical School, University of Edinburgh, Doorway 3, Teviot Place, Edinburgh, EH8 9AG, UK.
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The Role of Imaging in Preventive Cardiology in Women. Curr Cardiol Rep 2023; 25:29-40. [PMID: 36576679 DOI: 10.1007/s11886-022-01828-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/26/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE OF REVIEW The prevalence of CVD in women is increasing and is due to the increased prevalence of CV risk factors. Traditional CV risk assessment tools for prevention have failed to accurately determine CVD risk in women. CAC has shown to more precisely determine CV risk and is a better predictor of CV outcomes. Coronary CTA provides an opportunity to determine the presence of CAD and initiate prevention in women presenting with angina. Identifying women with INOCA due to CMD with use of cPET or cMRI with MBFR is vital in managing these patients. This review article outlines the role of imaging in preventive cardiology for women and will include the latest evidence supporting the use of these imaging tests for this purpose. RECENT FINDINGS CV mortality is higher in women who have more extensive CAC burden. Women have a greater prevalence of INOCA which is associated with higher MACE. INOCA is due to CMD in most cases which is associated with traditional CVD risk factors. Over half of these women are untreated or undertreated. Recent study showed that stratified medical therapy, tailored to the specific INOCA endotype, is feasible and improves angina in women. Coronary CTA is useful in the setting of women presenting with acute chest pain to identify CAD and initiate preventive therapy. CAC confers greater relative risk for CV mortality in women versus (vs.) men. cMRI or cPET is useful to assess MBFR to diagnose CMD and is another useful imaging tool in women for CV prevention.
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Livingstone SJ, Guthrie B, McMinn M, Eke C, Donnan PT, Morales DR. Derivation and validation of the CFracture competing risk fracture prediction tool compared with QFracture in older people and people with comorbidity: a population cohort study. THE LANCET. HEALTHY LONGEVITY 2023; 4:e43-e53. [PMID: 36610448 DOI: 10.1016/s2666-7568(22)00290-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND UK guidelines recommend the QFracture tool to predict the risk of major osteoporotic fracture and hip fracture, but QFracture calibration is poor, partly because it does not account for competing mortality risk. The aim of this study was to derive and validate a competing risk model to predict major osteoporotic fracture and hip fracture (CFracture) and compare its performance with that of QFracture in UK primary care. METHODS We used UK linked primary care data from the Clinical Practice Research Datalink GOLD database to identify people aged 30-99 years, split into derivation and validation cohorts. In the derivation cohort, we derived models (CFracture) using the same covariates as QFracture with Fine-Gray competing risk modelling, and included the Charlson Comorbidity Index score as an additional predictor of non-fracture death. In a separate validation cohort, we examined discrimination (using Harrell's C-statistic) and calibration of CFracture compared with QFracture. Reclassification analysis examined differences in the characteristics of patients reclassified as higher risk by CFracture but not by QFracture. FINDINGS The derivation cohort included 1 831 606 women and 1 789 820 men, and the validation cohort included 915 803 women and 894 910 men. Overall discrimination of CFracture was excellent (C-statistic=0·813 [95% CI 0·810-0·816] for major osteoporotic fracture and 0·914 [0·908-0·919] for hip fracture in women; 0·734 [0·729-0·740] for major osteoporotic fracture and 0·886 [0·877-0·895] for hip fracture in men) and was similar to QFracture. CFracture calibration overall and in people younger than 75 years was generally excellent. CFracture overpredicted major osteoporotic fracture and hip fracture in older people and people with comorbidity, but was better calibrated than QFracture. Patients classified as high-risk by CFracture but not by QFracture had a higher prevalence of current smoking and previous fracture, but lower prevalence of dementia, cancer, cardiovascular disease, renal disease, and diabetes. INTERPRETATION CFracture has similar discrimination to QFracture but is better calibrated overall and in younger people. Both models performed poorly in adults aged 85 years and older. Competing risk models should be recommended for fracture risk prediction to guide treatment recommendations. FUNDING National Institute for Health and Care Research, Wellcome Trust, Health Data Research UK.
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Affiliation(s)
- Shona J Livingstone
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Megan McMinn
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Chima Eke
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peter T Donnan
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK; Department of Public Health, University of Southern Denmark, Odense, Denmark.
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Stroke risk in older British men: Comparing performance of stroke-specific and composite-CVD risk prediction tools. Prev Med Rep 2022; 31:102098. [PMID: 36820364 PMCID: PMC9938339 DOI: 10.1016/j.pmedr.2022.102098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/14/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022] Open
Abstract
Stroke risk is currently estimated as part of the composite risk of cardiovascular disease (CVD). We investigated if composite-CVD risk prediction tools QRISK3 and Pooled Cohort Equations-PCE, derived from middle-aged adults, are as good as stroke-specific Framingham Stroke Risk Profile-FSRP and QStroke for capturing the true risk of stroke in older adults. External validation for 10y stroke outcomes was performed in men (60-79y) of the British Regional Heart Study. Discrimination and calibration were assessed in separate validation samples (FSRP n = 3762, QStroke n = 3376, QRISK3 n = 2669 and PCE n = 3047) with/without adjustment for competing risks. Sensitivity/specificity were examined using observed and clinically recommended thresholds. Performance of FSRP, QStroke and QRISK3 was further compared head-to-head in 2441 men free of a range of CVD, including across age-groups. Observed 10y risk (/1000PY) ranged from 6.8 (hard strokes) to 11 (strokes/transient ischemic attacks). All tools discriminated weakly, C-indices 0.63-0.66. FSRP and QStroke overestimated risk at higher predicted probabilities. QRISK3 and PCE showed reasonable calibration overall with minor mis-estimations across the risk range. Performance worsened on adjusting for competing non-stroke deaths. However, in men without CVD, QRISK3 displayed relatively better calibration for stroke events, even after adjustment for competing deaths, including in oldest men. All tools displayed similar sensitivity (63-73 %) and specificity (52-54 %) using observed risks as cut-offs. When QRISK3 and PCE were evaluated using thresholds for CVD prevention, sensitivity for stroke events was 99 %, with false positive rate 97 % suggesting existing intervention thresholds may need to be re-examined to reflect age-related stroke burden.
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Key Words
- AF, atrial fibrillation
- BRHS, British Regional Heart Study
- CHD, coronary heart disease
- CIF, cumulative incidence function
- CPI, centred prognostic index
- CVD, cardiovascular disease
- Calibration
- Cardiovascular disease
- Discrimination
- FSRP, Framingham stroke risk profile
- HF, heart failure
- KM, Kaplan-Meier
- MI, myocardial infarction
- NICE, National Institute For Health And Care Excellence
- Older adults
- PCE, pooled cohort equations
- PI, prognostic index
- Risk prediction
- SCORE, systematic coronary risk evaluation
- Sn/Sp, percent sensitivity/percent specificity
- Stroke
- TIA, transient ischemic attack
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13
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Livingstone SJ, Morales DR, McMinn M, Eke C, Donnan P, Guthrie B. Effect of competing mortality risks on predictive performance of the QFracture risk prediction tool for major osteoporotic fracture and hip fracture: external validation cohort study in a UK primary care population. BMJ MEDICINE 2022; 1:e000316. [PMID: 36936595 PMCID: PMC9978756 DOI: 10.1136/bmjmed-2022-000316] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022]
Abstract
Objective To externally evaluate the QFracture risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture. Design External validation cohort study. Setting UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016. Participants 2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year. Main outcome measures Two outcomes were modelled based on the QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture. Results 2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell's C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell's C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture. Conclusions The QFracture risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The use of QFracture in its current form needs to be reviewed, particularly in people at high risk of death from other causes.
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Affiliation(s)
| | - Daniel R Morales
- Population Health and Genomics Division, University of Dundee, Dundee, UK
| | - Megan McMinn
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Chima Eke
- Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK
| | - Peter Donnan
- Population Health and Genomics Division, University of Dundee, Dundee, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, University of Edinburgh, Edinburgh, UK
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14
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Subirana I, Camps-Vilaró A, Elosua R, Marrugat J, Tizón-Marcos H, Palomo I, Dégano IR. Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or Competing Risks. Clin Epidemiol 2022; 14:1145-1154. [PMID: 36254303 PMCID: PMC9569159 DOI: 10.2147/clep.s374581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Aims Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, their discrimination ability is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there are no data on CV risk functions including time-dependent covariates together with other variables. Our aim was to examine the effect of including time-dependent covariates, competing risks, and treatments in coronary risk prediction. Methods Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35-74 years without previous history of cardiovascular disease were included (n = 8470). Coronary and stroke events and mortality due to other CV causes or to cancer were recorded during follow-up (median = 12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with basal measurement of classical risk factors, treatments, or competing risks. Models were cross-validated and compared for discrimination (area under ROC curve), calibration (Hosmer-Lemeshow test), and reclassification (categorical net reclassification index). Results Cancer mortality was the highest cumulative-incidence event. Adding cholesterol and hypertension treatment to classical risk factors improved discrimination of coronary events by 2% and reclassification by 7-9%. The inclusion of competing risks and/or 2 measurements of risk factors provided similar coronary event prediction, compared to a single measurement of risk factors. Conclusion Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary risk prediction does not improve with 2 measurements of covariates or inclusion of competing risks.
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Affiliation(s)
- Isaac Subirana
- REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Anna Camps-Vilaró
- REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Roberto Elosua
- Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Medicine, University of Vic-Central University of Catalonia (Uvic-UCC), Vic, Spain,Cardiovascular Epidemiology and Genetics Group, Department of Epidemiology and Public Health, IMIM, Barcelona, Spain
| | - Jaume Marrugat
- REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Helena Tizón-Marcos
- Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiology Department, Hospital del Mar, Barcelona, Spain,Biomedical Research in Heart Diseases Group, Department of Translational Clinical Research, IMIM, Barcelona, Spain
| | - Ivan Palomo
- Department of Clinical Biochemistry and Immunohematology, Thrombosis Research Center, Faculty of Health Sciences, Medical Technology School, Talca, Chile
| | - Irene R Dégano
- REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Medicine, University of Vic-Central University of Catalonia (Uvic-UCC), Vic, Spain,Correspondence: Irene R Dégano, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute, Dr. Aiguader 88, 1 Floor office 122.10, Barcelona, 08003, Spain, Email
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15
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Tsuda T, Hayashi K, Kato T, Usuda K, Kusayama T, Nomura A, Tada H, Usui S, Sakata K, Kawashiri MA, Fujino N, Yamagishi M, Takamura M. Clinical Characteristics, Outcomes, and Risk Factors for Adverse Events in Elderly and Non-Elderly Japanese Patients With Non-Valvular Atrial Fibrillation ― Competing Risk Analysis From the Hokuriku-Plus AF Registry ―. Circ Rep 2022; 4:298-307. [PMID: 35860347 PMCID: PMC9257453 DOI: 10.1253/circrep.cr-22-0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/11/2022] [Accepted: 05/21/2022] [Indexed: 11/09/2022] Open
Abstract
Background: Few studies in Japan have reported on follow-up data regarding the clinical course and risk factors for adverse outcomes in elderly patients with non-valvular atrial fibrillation (NVAF), vs. younger patients, when considering the competing risk of death. Methods and Results: We prospectively studied 1,328 patients with NVAF (965 men; mean [±SD] age 72.4±9.7 years) from the Hokuriku-Plus AF Registry with a median follow-up of 5.0 years (interquartile range 3.5–5.3 years) and evaluated the incidence of thromboembolism or major bleeding in elderly (age ≥75 years; n=595) and non-elderly (age <75 years; n=733) patients. Analysis using the Gray method showed no significant difference in the incidence of thromboembolism; however, the incidence of major bleeding was significantly higher in the elderly than non-elderly group. The Fine-Gray model, after adjustment for age and sex in the elderly group, showed that age (hazard ratio [HR] 1.08; 95% confidence interval [CI] 1.02–1.13; P=0.004) and warfarin use (HR 1.87; 95% CI 1.12–3.14; P=0.02) were significantly associated with major bleeding. In the elderly group, those using warfarin had a higher incidence of thromboembolism and major bleeding than those using direct oral anticoagulants (DOACs). Conclusions: The efficacy and safety of DOACs were remarkable in elderly compared with non-elderly patients with NVAF considering the competing risk of death. DOACs may be a favorable choice in elderly patients with NVAF.
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Affiliation(s)
- Toyonobu Tsuda
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Kenshi Hayashi
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Takeshi Kato
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Keisuke Usuda
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Takashi Kusayama
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Akihiro Nomura
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Hayato Tada
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Soichiro Usui
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Kenji Sakata
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Masa-aki Kawashiri
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | - Noboru Fujino
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
| | | | - Masayuki Takamura
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences
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Tsarapatsani K, Sakellarios AI, Pezoulas VC, Tsakanikas VD, Kleber ME, Marz W, Michalis LK, Fotiadis DI. Machine Learning Models for Cardiovascular Disease Events Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1066-1069. [PMID: 36085658 DOI: 10.1109/embc48229.2022.9871121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical and machine learning (ML) models, In this work, we utilize clinical CVD risk factors and biochemical data using machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) and Adaptive Boosting (AdaBoost) to predict death caused by CVD within ten years of follow-up. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and 2943 patients were included in the analysis (484 annotated as dead due to CVD). We calculated the Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. The findings of the comparative analysis show that Logistic Regression has been proven to be the most reliable algorithm having accuracy 72.20 %. These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.
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17
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Meah MN, Maurovich-Horvat P, Williams MC, Newby DE. Debates in cardiac CT: Coronary CT angiography is the best test in asymptomatic patients. J Cardiovasc Comput Tomogr 2022; 16:290-293. [PMID: 35216929 DOI: 10.1016/j.jcct.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 12/30/2022]
Abstract
Cardiovascular disease remains a major cause of mortality, accounting for a third of all global deaths annually. Although there have been major improvements in our ability to detect and to treat patients with coronary heart disease, most myocardial infarctions occur in previously asymptomatic individuals. Identification of individuals at risk of myocardial infarction remains challenging and primary prevention guidelines rely on the use of cardiovascular risk scores that can be supplemented by coronary artery calcium scores. Coronary artery calcium scores provide a simple surrogate late marker of atherosclerosis but is unable to identify the early high risk non-calcified plaque which can be particularly problematic in younger individuals. Coronary computed tomography angiography is increasingly being used as the imaging strategy of choice in patients with symptoms of coronary heart disease. As an anatomical test, it can non-invasively detect the presence of coronary atherosclerosis, providing clinicians with a strong mandate to commence symptom relieving and preventative therapies. For asymptomatic individuals, it allows precise targeting of therapies to those with coronary heart disease rather than those "at risk" of disease. Moreover, our ability to calculate risk using coronary computed tomography angiography is rapidly improving with the use of techniques, such as plaque quantification and characterisation. These techniques have the potential to provide clinicians with tools to target cardiovascular disease prevention in a precision medicine approach. We here debate the ways in which coronary computed tomography angiography could improve the selection of asymptomatic individuals for preventative therapies over and above risk calculators and calcium scoring.
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Affiliation(s)
- Mohammed N Meah
- BHF Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | | | - Michelle C Williams
- BHF Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging, Queen's Medical Research Institute University of Edinburgh, Edinburgh, UK
| | - David E Newby
- BHF Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging, Queen's Medical Research Institute University of Edinburgh, Edinburgh, UK.
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18
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Livingstone SJ, Guthrie B, Donnan PT, Thompson A, Morales DR. Predictive performance of a competing risk cardiovascular prediction tool CRISK compared to QRISK3 in older people and those with comorbidity: population cohort study. BMC Med 2022; 20:152. [PMID: 35505353 PMCID: PMC9066924 DOI: 10.1186/s12916-022-02349-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/23/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Recommended cardiovascular disease (CVD) prediction tools do not account for competing mortality risk and over-predict incident CVD in older and multimorbid people. The aim of this study was to derive and validate a competing risk model (CRISK) to predict incident CVD and compare its performance to that of QRISK3 in UK primary care. METHODS We used UK linked primary care data from the Clinical Practice Research Datalink (CPRD) GOLD to identify people aged 25-84 years with no previous CVD or statin treatment split into derivation and validation cohorts. In the derivation cohort, we derived models using the same covariates as QRISK3 with Fine-Gray competing risk modelling alone (CRISK) and with Charlson Comorbidity score (CRISK-CCI) as an additional predictor of non-CVD death. In a separate validation cohort, we examined discrimination and calibration compared to QRISK3. Reclassification analysis examined the number of patients recommended for treatment and the estimated number needed to treat (NNT) to prevent a new CVD event. RESULTS The derivation and validation cohorts included 989,732 and 494,865 women and 946,784 and 473,392 men respectively. Overall discrimination of CRISK and CRISK-CCI were excellent and similar to QRISK3 (for women, C-statistic = 0.863/0.864/0.863 respectively; for men 0.833/0.819/0.832 respectively). CRISK and CRISK-CCI calibration overall and in younger people was excellent. CRISK over-predicted in older and multimorbid people although performed better than QRISK3, whilst CRISK-CCI performed the best. The proportion of people reclassified by CRISK-CCI varied by QRISK3 risk score category, with 0.7-9.7% of women and 2.8-25.2% of men reclassified as higher risk and 21.0-69.1% of women and 27.1-57.4% of men reclassified as lower risk. Overall, CRISK-CCI recommended fewer people for treatment and had a lower estimated NNT at 10% risk threshold. Patients reclassified as higher risk were younger, had lower SBP and higher BMI, and were more likely to smoke. CONCLUSIONS CRISK and CRISK-CCI performed better than QRISK3. CRISK-CCI recommends fewer people for treatment and has a lower NNT to prevent a new CVD event compared to QRISK3. Competing risk models should be recommended for CVD primary prevention treatment recommendations.
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Affiliation(s)
- Shona J Livingstone
- Division of Population Health and Genomics, University of Dundee, Mackenzie Building, Kirsty Semple Way, Dundee, UK
| | - Bruce Guthrie
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peter T Donnan
- Division of Population Health and Genomics, University of Dundee, Mackenzie Building, Kirsty Semple Way, Dundee, UK
| | - Alexander Thompson
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, UK
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Mackenzie Building, Kirsty Semple Way, Dundee, UK. .,Department of Public Health, University of Southern Denmark, Odense, Denmark.
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Ruchman SG, Delong AK, Kamano JH, Bloomfield GS, Chrysanthopoulou SA, Fuster V, Horowitz CR, Kiptoo P, Matelong W, Mugo R, Naanyu V, Orango V, Pastakia SD, Valente TW, Hogan JW, Vedanthan R. Egocentric social network characteristics and cardiovascular risk among patients with hypertension or diabetes in western Kenya: a cross-sectional analysis from the BIGPIC trial. BMJ Open 2021; 11:e049610. [PMID: 34475172 PMCID: PMC8413931 DOI: 10.1136/bmjopen-2021-049610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 08/11/2021] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES Management of cardiovascular disease (CVD) is an urgent challenge in low-income and middle-income countries, and interventions may require appraisal of patients' social networks to guide implementation. The purpose of this study is to determine whether egocentric social network characteristics (SNCs) of patients with chronic disease in western Kenya are associated with overall CVD risk and individual CVD risk factors. DESIGN Cross-sectional analysis of enrollment data (2017-2018) from the Bridging Income Generation with GrouP Integrated Care trial. Non-overlapping trust-only, health advice-only and multiplex (trust and health advice) egocentric social networks were elicited for each participant, and SNCs representing social cohesion were calculated. SETTING 24 communities across four counties in western Kenya. PARTICIPANTS Participants (n=2890) were ≥35 years old with diabetes (fasting glucose ≥7 mmol/L) or hypertension. PRIMARY AND SECONDARY OUTCOMES We hypothesised that SNCs would be associated with CVD risk status (QRISK3 score). Secondary outcomes were individual CVD risk factors. RESULTS Among the 2890 participants, 2020 (70%) were women, and mean (SD) age was 60.7 (12.1) years. Forty-four per cent of participants had elevated QRISK3 score (≥10%). No relationship was observed between QRISK3 level and SNCs. In unadjusted comparisons, participants with any individuals in their trust network were more likely to report a good than a poor diet (41% vs 21%). SNCs for the trust and multiplex networks accounted for a substantial fraction of variation in measures of dietary quality and physical activity (statistically significant via likelihood ratio test, adjusted for false discovery rate). CONCLUSION SNCs indicative of social cohesion appear to be associated with individual behavioural CVD risk factors, although not with overall CVD risk score. Understanding how SNCs of patients with chronic diseases relate to modifiable CVD risk factors could help inform network-based interventions. TRIAL REGISTRATION NUMBER ClinicalTrials.gov identifier: NCT02501746; https://clinicaltrials.gov/ct2/show/NCT02501746.
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Affiliation(s)
- Samuel G Ruchman
- Department of Medicine, Department of Pediatrics, University of Colorado, Aurora, Colorado, USA
| | - Allison K Delong
- Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Jemima H Kamano
- Department of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | | | | | - Valentin Fuster
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Carol R Horowitz
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Peninah Kiptoo
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Winnie Matelong
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Richard Mugo
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Violet Naanyu
- Department of Sociology, Psychology and Anthropology, School of Arts and Social Sciences, Moi University, Eldoret, Kenya
| | - Vitalis Orango
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Sonak D Pastakia
- Department of Pharmacy Practice, Purdue University, West Lafayette, Indiana, USA
| | - Thomas W Valente
- Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA
| | - Joseph W Hogan
- Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Rajesh Vedanthan
- Department of Population Health, NYU Grossman School of Medicine, New York City, New York, USA
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Challenges of treating cardiovascular risk in old age. THE LANCET. HEALTHY LONGEVITY 2021; 2:e308-e309. [DOI: 10.1016/s2666-7568(21)00114-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 04/27/2021] [Indexed: 01/13/2023] Open
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