Prediction models for cardiovascular disease risk in the general population: systematic reviewBMJ 2016; 353 doi: https://doi.org/10.1136/bmj.i2416 (Published 16 May 2016) Cite this as: BMJ 2016;353:i2416
- Johanna A A G Damen, PhD fellow1 2,
- Lotty Hooft, associate professor 1 2,
- Ewoud Schuit, postdoctoral researcher1 2 3,
- Thomas P A Debray, assistant professor1 2,
- Gary S Collins, associate professor4,
- Ioanna Tzoulaki, lecturer5,
- Camille M Lassale, research associate in chronic disease epidemiology5,
- George C M Siontis, research associate6,
- Virginia Chiocchia, medical statistician4 7,
- Corran Roberts, medical statistician4,
- Michael Maia Schlüssel, medical statistician4,
- Stephen Gerry, medical statistician4,
- James A Black, epidemiologist8,
- Pauline Heus, researcher1 2,
- Yvonne T van der Schouw, professor1,
- Linda M Peelen, assistant professor1,
- Karel G M Moons, professor1 2
- 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
- 2Cochrane Netherlands, University Medical Center Utrecht, PO Box 85500, Str 6.131, 3508 GA Utrecht, Netherlands
- 3Stanford Prevention Research Center, Stanford University, Stanford, CA, USA
- 4Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- 5Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- 6Department of Cardiology, Bern University Hospital, 3010 Bern, Switzerland
- 7Surgical Intervention Trials Unit, University of Oxford, Oxford, UK
- 8MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Correspondence to: J A A G Damen
- Accepted 19 April 2016
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.
We thank René Spijker for performing the literature search and Johannes B Reitsma who provided insight and expertise that greatly assisted this project.
Contributors: KGMM and ES designed the study. All authors selected articles or extracted data. JAAGD analysed the data. JAAGD, LH, TPAD, IT, CML, YTS, LMP, ES, and KGM interpreted the data. JAAGD wrote the first draft of the manuscript, which was revised by all authors. All authors approved the final version of the submitted manuscript. All authors had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. JAAGD is guarantor.
Funding: KGMM, JAAGD, LH, ES, and TPAD were supported by various grants from The Netherlands Organization for Scientific Research, Dutch Heart Foundation, and the Cochrane Collaboration. KGMM received a grant from The Netherlands Organization for Scientific Research (ZONMW 918.10.615 and 91208004). GSC was supported by MRC grant G1100513. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 279233. None of the funding sources had a role in the design, conduct, analyses, or reporting of the study or in the decision to submit the manuscript for publication.
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.
Ethical approval: Not required.
Data sharing: No additional data available.
Transparency: The lead authors affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
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