Abstract
Background: Cardiovascular disease (CVD) is a leading cause of morbidity and the leading cause of mortality worldwide. Many prediction models have been developed to assess individual CVD risk to allow targeting of preventive treatment.
Objectives: To provide an overview of all prognostic models that predict future risk of CVD in the general population, and to describe their reporting on predicted outcomes, study populations, predictors, and methods.
Methods: In June 2013 a systematic search was performed in Medline and Embase to identify studies that described the development or external validation of a model predicting CVD in the general population.
Results: 9965 references were identified, of which 1388 were screened in full text. 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 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 the majority of models was sex-specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models and important clinical and methodological information was often missing. For 49 models (13%) the prediction time horizon was not specified and for 92 (25%) crucial information was missing to actually use the model for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was very heterogeneous and measures such as discrimination and calibration were reported for 65% and 58% of the external validations respectively.
Conclusion: There is an excess of models predicting incident CVD in the general population. The usefulness of the majority of the models remains unclear due to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, future research should focus on externally validating and head-to-head comparisons of the promising existing CVD risk models, on tailoring these models to local settings or even combining them, and investigating whether they can be extended by addition of new predictors.
Objectives: To provide an overview of all prognostic models that predict future risk of CVD in the general population, and to describe their reporting on predicted outcomes, study populations, predictors, and methods.
Methods: In June 2013 a systematic search was performed in Medline and Embase to identify studies that described the development or external validation of a model predicting CVD in the general population.
Results: 9965 references were identified, of which 1388 were screened in full text. 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 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 the majority of models was sex-specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models and important clinical and methodological information was often missing. For 49 models (13%) the prediction time horizon was not specified and for 92 (25%) crucial information was missing to actually use the model for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was very heterogeneous and measures such as discrimination and calibration were reported for 65% and 58% of the external validations respectively.
Conclusion: There is an excess of models predicting incident CVD in the general population. The usefulness of the majority of the models remains unclear due to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, future research should focus on externally validating and head-to-head comparisons of the promising existing CVD risk models, on tailoring these models to local settings or even combining them, and investigating whether they can be extended by addition of new predictors.
Original language | English |
---|---|
Publication status | Published - 2016 |
Event | Methods for Evaluating Medical Tests and Biomarkers Symposium - University of Birmingham, Birmingham, United Kingdom Duration: 19 Jul 2016 → 20 Jul 2016 http://www.birmingham.ac.uk/research/activity/mds/projects/HaPS/PHEB/diagnostic-research/conferences/symposium-2016.aspx |
Conference
Conference | Methods for Evaluating Medical Tests and Biomarkers Symposium |
---|---|
Abbreviated title | MEMTAB |
Country/Territory | United Kingdom |
City | Birmingham |
Period | 19/07/16 → 20/07/16 |
Internet address |