TY - JOUR
T1 - Cardiovascular risk prediction models for women in the general population
T2 - A systematic review
AU - Baart, Sara J.
AU - Dam, Veerle
AU - Scheres, Luuk J.J.
AU - Damen, Johanna A.A.G.
AU - Spijker, René
AU - Schuit, Ewoud
AU - Debray, Thomas P.A.
AU - Fauser, Bart C.J.M.
AU - Boersma, Eric
AU - the CREW Consortium
AU - Moons, Karel G.M.
AU - van der Schouw, Yvonne T.
N1 - Publisher Copyright:
© 2019 Baart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/1/8
Y1 - 2019/1/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059752073&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0210329
DO - 10.1371/journal.pone.0210329
M3 - Article
C2 - 30620772
AN - SCOPUS:85059752073
SN - 1932-6203
VL - 14
JO - PLoS ONE
JF - PLoS ONE
IS - 1
M1 - e0210329
ER -
