Cardiovascular risk prediction models for women in the general population: A systematic review

Sara J Baart, Veerle Dam, Luuk J J Scheres, Johanna A A G Damen, René Spijker, Ewoud Schuit, Thomas P A Debray, Bart C J M Fauser, Eric Boersma, Karel G M Moons, Yvonne T van der Schouw and on behalf of the CREW Consortium

PLOS ONE, 2019, vol. 14, issue 1, 1-14

Abstract: Aim: To provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors. Methods: We performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model. Results: A total of 285 prediction models for women have been developed, of these 160 (56%) were female-specific models, in which a separate model was developed solely in women and 125 (44%) were sex-predictor models. Out of the 160 female-specific models, 2 (1.3%) included one or more female-specific predictors (mostly reproductive risk factors). A total of 591 validations of sex-predictor or female-specific models were identified in 206 papers. Of these, 333 (56%) validations concerned nine models (five versions of Framingham, SCORE, Pooled Cohort Equations and QRISK). The median and pooled C statistics were comparable for sex-predictor and female-specific models. In 260 articles the added value of new predictors to an existing model was described, however in only 3 of these female-specific predictors (reproductive risk factors) were added. Conclusions: There is an abundance of models for women in the general population. Female-specific and sex-predictor models have similar predictors and performance. Female-specific predictors are rarely included. Further research is needed to assess the added value of female-specific predictors to CVD models for women and provide physicians with a well-performing prediction model for women.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0210329

DOI: 10.1371/journal.pone.0210329

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