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BMJ. 2016 May 16;353:i2416. doi: 10.1136/bmj.i2416.

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

Author information

1
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands Cochrane Netherlands, University Medical Center Utrecht, PO Box 85500, Str 6.131, 3508 GA Utrecht, Netherlands j.a.a.damen@umcutrecht.nl.
2
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands Cochrane Netherlands, University Medical Center Utrecht, PO Box 85500, Str 6.131, 3508 GA Utrecht, Netherlands.
3
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands Cochrane Netherlands, University Medical Center Utrecht, PO Box 85500, Str 6.131, 3508 GA Utrecht, Netherlands Stanford Prevention Research Center, Stanford University, Stanford, CA, USA.
4
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
5
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
6
Department of Cardiology, Bern University Hospital, 3010 Bern, Switzerland.
7
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK Surgical Intervention Trials Unit, University of Oxford, Oxford, UK.
8
MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
9
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands.

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.

PMID:
27184143
PMCID:
PMC4868251
DOI:
10.1136/bmj.i2416
[Indexed for MEDLINE]
Free PMC Article

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