Damen et al. (1) undertook a systematic review in order to provide an overview of prediction models for risk of cardiovascular disease in the general population. They searched Medline and Embase until June 2013 and found 9 965 articles, of which 212 were included in their review. These articles described 363 prediction models and 473 external validations.

Most of these models were developed in Europe (n=167, 46%) and predicted risk of coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%).  Common predictors were smoking (n=325, 90%) and age (n=321, 88%). Most of the models were sex specific (n=250, 69%).

The authors found substantial heterogeneity in predictor and outcome definitions, and important information was often missing. For 49 models (13%) the prediction horizon was not specified, and for 92 (25%) crucial information for enabling the model to be used for individual risk prediction was missing.

No more than 132 models (36%) were externally validated and only 70 (19%) by independent investigators. The model performance was heterogeneous and discrimination and calibration were only reported for 65% and 58% of the external validations, respectively.

The authors conclude that there is an excess of models predicting cardiovascular disease in the general population, and that the usefulness of most of these is unclear because of methodological shortcomings, incomplete presentation, lack of external validation, and lack of model impact studies. Future work should primarily focus on external validation and comparisons of already existing models.



  1. Damen JAAG, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 2016;353:i2416.