Updating risk prediction tools a case study in prostate cancer

09-Sep-2020 13:55

Objective To provide an overview of prediction models for risk of cardiovascular disease (CVD) 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.

Eligibility criteria for study selection Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population.

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.

Articles published before 2004 were identified from a previously published comprehensive systematic review,12 and a cross reference check was performed for all reviews on CVD prediction models identified by our search.

For external validation studies where the development study was not identified by our search, we manually retrieved and included in the review the original article describing the development of the model.

Additionally, two presented models yielding the same predictor-outcome associations but with a different baseline hazard or risk estimate, were considered separately.

Initially pairs of two reviewers (JAB, TPAD, CML, LMP, ES, GCMS) independently screened retrieved articles for eligibility on title and subsequently on abstract.

updating risk prediction tools a case study in prostate cancer-64

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Several reviews have shown that there is an abundance of prediction models for a wide range of CVD outcomes.12 13 14 However, the most comprehensive review12 includes models published more than 10 years ago (search carried out in 2003).Over the past two decades, numerous prediction models have been developed, which mathematically combine multiple predictors to estimate the risk of developing CVD—for example, the Framingham,3 4 5 SCORE,6 and QRISK7 8 9 models.

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