Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study.

Autor:	Doust J; Australian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Australia., Baneshi MR; Australian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Australia., Chung HF; Australian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Australia., Wilson LF; Australian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Australia., Mishra GD; Australian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Australia.
Jazyk:	angličtina
Zdroj:	Circulation. Cardiovascular quality and outcomes [Circ Cardiovasc Qual Outcomes] 2024 Dec; Vol. 17 (12), pp. e010842. Date of Electronic Publication: 2024 Dec 06.
DOI:	10.1161/CIRCOUTCOMES.123.010842
Abstrakt:	Background: Cardiovascular disease (CVD) is the leading cause of mortality in women. We aimed to assess whether adding female-specific risk factors to traditional factors could improve CVD risk prediction. Methods: We used a cohort of women from the UK Biobank Study aged 45 to 69 years, free of CVD at baseline (2006-2010) followed until the end of 2019. We developed Cox proportional hazards models using the risk factors included in 3 contemporary CVD risk calculators: Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT. We added each of the following female-specific risk factors, individually and all together, to determine if these improved measures of discrimination and calibration for predicting CVD: early menarche (<11 years), endometriosis, excessive, frequent or irregular menstruation, miscarriage, number of miscarriages, number of stillbirths, infertility, preeclampsia or eclampsia, gestational diabetes (without subsequent type 2 diabetes), premature menopause (<40 years), early menopause (<45 years), and natural or surgical early menopause (menopause <45 years or timing of menopause reported as unknown and oophorectomy reported at age <45). Results: In the model of 135 142 women (mean age, 57.5 years; SD, 6.8) using risk factors from Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, CVD incidence was 5.3 per 1000 person-years. The c-indices for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models were 0.710, 0.713, and 0.718, respectively. Adding each of the female-specific risk factors did not improve the c-index, the net reclassification index, the integrated discrimination index, the slope of the regression line for predicted versus observed events, and the Brier score or plots of calibration. Adding all female-specific risk factors simultaneously increased the c-index for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models to 0.712, 0.715, and 0.720, respectively. Conclusions: Although several female-specific factors have been shown to be early indicators of CVD risk, these factors should not be used to reclassify risk in women aged 45 to 69 years when considering whether to commence a blood pressure or lipid-lowering medication. Competing Interests: None.
Databáze:	MEDLINE
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