The DOI matches its Crossref record; 98 of the 111 references it cites were found in a registry or an index.
Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis
Open accessAbstract
BACKGROUND: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies. METHODS: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression. RESULTS: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics. CONCLUSIONS: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.
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![Fig. 4 Calibration plots of the Framingham Wilson, ATP III and PCE models. Each line represents one external validation. The diagonal line represents perfect agreement between observed and predicted risks. All points below that line indicate that more events were predicted than observed (overprediction) and points above the line indicate fewer events were predicted than observed (underprediction). The vertical black line represents a treatment threshold of 7.5% [68].](/paper-figures/fd6ce787f4444b7f9954e3fcf809933d/performance-of-the-framingham-risk-models-and-pooled-cohort-equations-for-figure-4.jpg)



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“Furthermore, the Framingham predicted 10-year CVD risk equations are well accepted and widely used in clinical practice.28”
From Tirzepatide compared with semaglutide and 10-year cardiovascular disease risk reduction in obesity: post-hoc analysis of the SURMOUNT-5 trial (2025) · cites this paper as [28]
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“Hal ini dipengaruhi selain karena heterogenitas populasi juga dipengaruhi oleh program perubahan gaya hidup dan pengobatan medis yang diterima oleh tiap populasi (Al-Shamsi, 2020; Damen et al., 2019).”
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“…[20] 。近期研 究 [21] 显示糖尿病患者硒水平升高与 CHD 高风险相关。 然而,另一项包括 16 个随机对照试验的荟萃分析 [22] 结果显示补充硒可减轻 CHD 的氧化应激和炎症反应, Framingham 风险评分对于 CHD 的预测能力存在局限 性 [23] 。一项荟萃分析 [24] 结果显示 Framingham 风险评 分、汇总队列方程等预测模型均高估了心血管疾病 发生的风险,尤其是在高危人群中。随着大数据分 析的深入,机器学习通过探索潜在因素之间的多维 非线性关系将多元的变量纳入 CHD 风险预测模型, 为个体化风险评估提供了新的方法 [25] 。一项基于…”
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Oxygen Sensors in Atherosclerosis
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Metadata
- Authors
- Damen, J. A., Pajouheshnia, R., Heus, P., Moons, K. G. M., Reitsma, J. B., Scholten, R. J. P. M., Hooft, L., & Debray, T. P. A.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Damen, Johanna A.
- ORCID
- Venue
- BMC Medicine
- Published
- 2019-06-13
- Volume
- 17
- Issue
- 1
- Pages
- 109-109
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC6563379
- OpenAlex
- W2953059279
- Cited by
- 250
- References
- 111
- Citations by year
- 2026: 33 · 2025: 45 · 2024: 37 · 2023: 41 · 2022: 37 · 2021: 27 · 2020: 25 · 2019: 3
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Cochrane Methods Innovation Funds Round 2 (MTH001F)
- Cochrane Trusted methods and Support for Cochrane Reviews of Prognostic studies (-)
- Netherlands Organization for Scientific Research (918.10.615, 91208004, 91617050)
- CREW NHS project (2013T083)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- Framingham risk score, Pooled Cohort Equations, cardiovascular risk prediction, coronary heart disease risk, risk overestimation, model calibration, discrimination (statistics), sex differences, population heterogeneity
- MeSH terms
- Aged, Cardiovascular Diseases, Female, Humans, Male, Models, Theoretical, Predictive Value of Tests, Prognosis, Risk Factors, Cohort Studies, Risk Assessment
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