ArticleEuropean journal of preventive cardiology2023
Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2.
Article in European journal of preventive cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Periodontal disease and subsequent risk of cardiovascular outcome and all-cause mortality: A meta-analysis of prospective studies.PloS one · 2023Pooled it
- Discrepancies in low-value practices across cardiovascular disease prevention guidelines. A narrative review.The European journal of general practice · 2026Review
- A next-generation prediction risk model for acute myocardial infarction: Derivation and validation in a multi-centre cohort.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- The 2025 ESC/EAS Dyslipidaemia Focused Update: Clinical Interpretation and Practical Implications.Cardiovascular drugs and therapy · 2026Review
- Machine-learning-based cardiovascular mortality prediction using a cumulative PMScientific reports · 2026Observational
- Diagnostic Accuracy of Cardiovascular Disease Prediction Models: A Systematic Review and Meta-Analysis of Validation Studies.Health science reports · 2026Review
- Prognostic value of PREVENT, PCE, and social determinants of health for cardiovascular mortality in the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study.BMC public health · 2026Article
- Risk prediction for individual patients and the pitfalls of selecting an optimal prediction model: do not judge a model by its c-statistic.European journal of epidemiology · 2026Article
- Using SCORE2 with a risk chart or online calculator: impact on model performance, treatment eligibility, and cardiovascular disease prevention.European heart journal. Quality of care & clinical outcomes · 2026Article
- External validation of the SMART2 model for recurrent cardiovascular risk: performance in socioeconomic, ethnic and psychiatric subgroups.Open heart · 2026Article
- Preventing Atherosclerotic Cardiovascular Disease in Young Male Androgen Abusers.Clinical cardiology · 2026Review
- Cardiovascular Prevention: Current Gaps and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Brazilian Guideline on Dyslipidemias and Prevention of Atherosclerosis - 2025.Arquivos brasileiros de cardiologia · 2025Article
- Development, testing and comparison of novel lifestyle-based prediction models for risk of coronary heart disease.Scientific reports · 2025Article
- Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence-Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium.JMIR research protocols · 2025Article
- Article
- Troponin Testing for Cardiovascular Primary Prevention Decision Making?Journal of the American College of Cardiology · 2025Article
- Cardiovascular Risk Assessment and Lipid-Lowering Therapy Recommendations in Primary Prevention.Journal of clinical medicine · 2025Review
- Risk prediction of cardiovascular disease in the Asia-Pacific region: the SCORE2 Asia-Pacific model.European heart journal · 2025Article
- Office-based risk equation of Globorisk for prediction of ten-years cardiovascular risk among Iranian population: findings from Fasa PERSIAN cohort study.BMC medical research methodology · 2024Article
Corrections and comments
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- Erratum issued
Authors and funding
20 authors at 10 institutions in 6 countries.
Funding
Abstract
aimsIn clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics. METHODS AND
resultsIndividuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)].
conclusionThe current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.
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