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

Damen, JA; Hooft, L; Schuit, E; Debray, TP; Collins, GS; Tzoulaki, I; Lassale, CM; ... Moons, KG; + view all (2016) Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ , 353 , Article i2416. 10.1136/bmj.i2416. Green open access

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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.

Type: Article
Title: Prediction models for cardiovascular disease risk in the general population: systematic review
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1136/bmj.i2416
Publisher version: http://dx.doi.org/10.1136/bmj.i2416
Language: English
Additional information: Copyright © BMJ Publishing Group Ltd, 2016. All rights reserved. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Further details about CC BY licenses are available at http://creativecommons.org/licenses/by/4.0
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Epidemiology and Public Health
URI: https://discovery.ucl.ac.uk/id/eprint/1493827
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