Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 May 16:353:i2416.
doi: 10.1136/bmj.i2416.

Prediction models for cardiovascular disease risk in the general population: systematic review

Johanna A A G Damen  1 , Lotty Hooft  2 , Ewoud Schuit  3 , Thomas P A Debray  2 , Gary S Collins  4 , Ioanna Tzoulaki  5 , Camille M Lassale  5 , George C M Siontis  6 , Virginia Chiocchia  7 , Corran Roberts  4 , Michael Maia Schlüssel  4 , Stephen Gerry  4 , James A Black  8 , Pauline Heus  2 , Yvonne T van der Schouw  9 , Linda M Peelen  9 , Karel G M Moons  2
Affiliations

Prediction models for cardiovascular disease risk in the general population: systematic review

Johanna A A G Damen et al. BMJ. .

Abstract

Objective: To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.

Design: Systematic review.

Data sources: Medline and Embase until June 2013.

Eligibility criteria for study selection: Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population.

Results: 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively.

Conclusions: There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.

PubMed Disclaimer

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

None
Fig 1 Flow diagram of selected articles
None
Fig 2 Numbers of articles in which only one or more models were developed (dark blue), only one or more models were externally validated (light blue), or one or more models were developed and externally validated (white), ordered by publication year (up to June 2013). Predictions of the total numbers in 2013 are displayed with dotted lines
None
Fig 3 Main categories of predictors included in developed models. CVD=cardiovascular disease; HDL=high density lipoprotein; LDL=low density lipoprotein
See this image and copyright information in PMC

Comment in

References

    1. Eckel RH, Jakicic JM, Ard JD, et al. American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129(Suppl 2):S76-99. 10.1161/01.cir.0000437740.48606.d1. pmid:24222015. - DOI - PubMed
    1. Alwan A. Global status report on noncommunicable diseases 2010: World Health Organization, 2011. http://apps.who.int/iris/bitstream/10665/44579/1/9789240686458_eng.pdf.
    1. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J 1991;121:293-8. 10.1016/0002-8703(91)90861-B. pmid:1985385. - DOI - PubMed
    1. Anderson KM, Wilson PW, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals. Circulation 1991;83:356-62. 10.1161/01.CIR.83.1.356. pmid:1984895. - DOI - PubMed
    1. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47. 10.1161/01.CIR.97.18.1837. pmid:9603539. - DOI - PubMed

Publication types