CoChat
Sign Up
Cited by 250Health Promotion and Cardiovascular PreventionLipoproteins and Cardiovascular HealthCardiovascular Health and Risk Factors

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 access
Damen, Johanna A., Pajouheshnia, Romin, Heus, Pauline, Moons, Karel G. M., Reitsma, Johannes B., Scholten, Rob J. P. M., Hooft, Lotty, Debray, Thomas P. A.·2019·BMC Medicine
DOI 10.1186/s12916-019-1340-7PMC PMC6563379
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1186/s12916-019-1340-7

Cites

98 of 98 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Figures

Fig. 1 Flow diagram of selected studies. Two searches were performed; one in MEDLINE and Embase and one in Scopus and Web of Science. Only studies identified by both searches were screened for eligibility, supplemented with records identified from previous systematic reviews. One study could describe more than one external validation (e.g. one for men and one for women); therefore, 61 studies described 167 external validations. Calibration was reported in 94 validations (41 directly reported, 19 provided by the authors on request, 34 estimated from calibration tables and calibration plots), and discrimination in 103 validations (91 c-statistics directly reported, 12 provided by the authors on request. Precision of c-statistic: 45 directly reported, 24 provided by the authors, 32 estimated from the sample size and 2 not reported). Some external validations were excluded because cohorts were used more than once to validate the same model (Additional file 9). *For example, no cardiovascular outcome and not written in English. †The Framingham Wilson and ATP III models were developed to predict the risk of fatal or nonfatal coronary heart disease, and the PCE model was developed to predict the risk of fatal or nonfatal cardiovascular disease. External validations that used a different outcome were excluded from the analyses (Additional file 8)
Fig. 1 Flow diagram of selected studies. Two searches were performed; one in MEDLINE and Embase and one in Scopus and Web of Science. Only studies identified by both searches were screened for eligibility, supplemented with records identified from previous systematic reviews. One study could describe more than one external validation (e.g. one for men and one for women); therefore, 61 studies described 167 external validations. Calibration was reported in 94 validations (41 directly reported, 19 provided by the authors on request, 34 estimated from calibration tables and calibration plots), and discrimination in 103 validations (91 c-statistics directly reported, 12 provided by the authors on request. Precision of c-statistic: 45 directly reported, 24 provided by the authors, 32 estimated from the sample size and 2 not reported). Some external validations were excluded because cohorts were used more than once to validate the same model (Additional file 9). *For example, no cardiovascular outcome and not written in English. †The Framingham Wilson and ATP III models were developed to predict the risk of fatal or nonfatal coronary heart disease, and the PCE model was developed to predict the risk of fatal or nonfatal cardiovascular disease. External validations that used a different outcome were excluded from the analyses (Additional file 8)CC BY · PubMed Central
Fig. 2 Risk of bias assessment. Summary of risk of bias assessments for validations included in the meta-analyses of OE ratio (74 validations) and c-statistic (77 validations)
Fig. 2 Risk of bias assessment. Summary of risk of bias assessments for validations included in the meta-analyses of OE ratio (74 validations) and c-statistic (77 validations)CC BY · PubMed Central
Fig. 3 Forest plots of the OE ratio in external validations. Ninety-five percent confidence intervals and 95% prediction intervals per model are indicated. The performance of the model in the development study is shown in the first rows (only reported for PCE). This estimate is not included in calculating the pooled estimate of performance. *Performance of the model in the development population after internal validation. The first row contains the performance of the model for Whites, the second for African Americans. **Standard error was not available. CHD: Coronary heart disease, CVD: cardiovascular disease
Fig. 3 Forest plots of the OE ratio in external validations. Ninety-five percent confidence intervals and 95% prediction intervals per model are indicated. The performance of the model in the development study is shown in the first rows (only reported for PCE). This estimate is not included in calculating the pooled estimate of performance. *Performance of the model in the development population after internal validation. The first row contains the performance of the model for Whites, the second for African Americans. **Standard error was not available. CHD: Coronary heart disease, CVD: cardiovascular diseaseCC BY · PubMed Central
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].
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].CC BY · PubMed Central
Fig. 5 Forest plots of c-statistic in external validations. Ninety-five percent confidence intervals and 95% prediction intervals per model are indicated. The performance of the model in the development study is shown in the first row(s) (not reported for the ATP III model) and is not included in the pooled estimate of performance. *Performance of the model in the development population (Wilson (no standard error reported)) and after 10 × 10 cross-validation (PCE). For the PCE, the first row contains the performance of the White model and the second the African American model. **Standard error was not available. CHD: coronary heart disease, CVD: cardiovascular disease
Fig. 5 Forest plots of c-statistic in external validations. Ninety-five percent confidence intervals and 95% prediction intervals per model are indicated. The performance of the model in the development study is shown in the first row(s) (not reported for the ATP III model) and is not included in the pooled estimate of performance. *Performance of the model in the development population (Wilson (no standard error reported)) and after 10 × 10 cross-validation (PCE). For the PCE, the first row contains the performance of the White model and the second the African American model. **Standard error was not available. CHD: coronary heart disease, CVD: cardiovascular diseaseCC BY · PubMed Central
Fig. 6 C-statistic for different combinations of eligibility criteria. The open squares, circles and triangles represent validations of the ATP III, PCE and Wilson model, respectively. The black circles and triangles represent the performance of the PCE models for Whites and African-Americans, and Wilson models, in the development populations. Lower part: for age, white means a broad age range was included (difference between upper and lower age limit > 30 years), black means a narrow age range was included (difference between upper and lower age limit ≤ 30 years) and grey means age was not reported. For CVD, white means no exclusion of people with CHD or CVD, grey means people with previous CHD events were excluded from the study and black means people with previous CVD events were excluded from the study. For diabetes, cancer and major disease, white means that no restrictions were reported and black means that people with these conditions were excluded. For treatment, white means no restrictions and black means people who were receiving any treatment to lower their risk of CVD (e.g. anti-hypertensives) were excluded from the study
Fig. 6 C-statistic for different combinations of eligibility criteria. The open squares, circles and triangles represent validations of the ATP III, PCE and Wilson model, respectively. The black circles and triangles represent the performance of the PCE models for Whites and African-Americans, and Wilson models, in the development populations. Lower part: for age, white means a broad age range was included (difference between upper and lower age limit > 30 years), black means a narrow age range was included (difference between upper and lower age limit ≤ 30 years) and grey means age was not reported. For CVD, white means no exclusion of people with CHD or CVD, grey means people with previous CHD events were excluded from the study and black means people with previous CVD events were excluded from the study. For diabetes, cancer and major disease, white means that no restrictions were reported and black means that people with these conditions were excluded. For treatment, white means no restrictions and black means people who were receiving any treatment to lower their risk of CVD (e.g. anti-hypertensives) were excluded from the studyCC BY · PubMed Central
Fig. 7 Performance of models before and after update. The x-axis is sorted by performance before updating. The lines connect performance of models in the same cohort before and after updating
Fig. 7 Performance of models before and after update. The x-axis is sorted by performance before updating. The lines connect performance of models in the same cohort before and after updatingCC BY · PubMed Central

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 111 references

Research works printed but not linked: 6

  1. Beswick AD, Brindle P, Fahey T, Ebrahim S. A systematic review of risk scoring methods and clinical decision aids used in the primary prevention of coronary heart disease (supplement). London: Royal College of General Practitioners; 2008.
  2. Harrell F. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Switzerland: Springer; 2015. · DOI · Paper page
  3. Debray TP. Metamisc: Diagnostic and Prognostic Meta-Analysis; 2017.
  4. Marrugat J, Subirana I, Comin E, Cabezas C, Vila J, Elosua R, Nam BH, Ramos R, Sala J, Solanas P, et al. Validity of an adaptation of the Framingham cardiovascular risk function: the VERIFICA Study. J Epidemiol Community Health. 2007;61(1):40–7. · DOI
  5. Knottnerus JA. Diagnostic prediction rules: principles, requirements and pitfalls. Prim Care. 1995;22(2):341–63. · DOI · Paper page
  6. Goff DC Jr, D’Agostino RB Sr, Pencina M, Lloyd-Jones DM. Calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med. 2015;163(1):68. · DOI

Cited by

Sorted by citations

Items 101–200 of 221

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
Publisher
Springer Science and Business Media LLC
Published
2019-06-13
Volume
17
Issue
1
Pages
109-109
ISSN
1741-7015 (ISSN-L)
DOI
10.1186/s12916-019-1340-7
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
Licence
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review