Added Predictive Value of Female-Specific Factors and Psychosocial Factors for the Risk of Stroke in Women Under 50

Abstract

Background:Female-specific factors and psychosocial factors may be important in the prediction of stroke, but are not included in prediction models that are currently used. We investigated whether addition of these factors would improve the performance of prediction models for the risk of stroke in women under 50.Patients & methods:We used data from the STIZON, population-based, primary care database of women aged 20–49 years without a history of cardiovascular disease. Analyses were stratified by 10-year age intervals at cohort entry. Cox proportional hazards models to predict stroke risk were developed, including traditional cardiovascular factors, and compared with models that additionally included female-specific and psychosocial factors. We compared the risk models using the C-statistic and the slope of the calibration curve at a follow-up of 10 years. We developed an age-specific stroke risk prediction tool that may help communicating the risk of stroke in clinical practice.Results:We included 409,026 women with a total of 3,990,185 person years of follow-up. Stroke occurred in 2,751 women (incidence rate 6.9 [95%CI:6.6–7.2] per 10,000 person years). Models with only traditional cardiovascular factors performed poorly to moderately in all age groups: 20–29 years: C-statistic: 0.617 (95%CI:0.592–0.639); 30–39 years: C-statistic: 0.615 (95%CI:0.596–0.634); 40–49 years: C-statistic: 0.585 (95%CI:0.573–0.597). After adding the female-specific and psychosocial risk factors to the reference models, the model discrimination increased moderately, especially in the age groups 30–39 (ΔC-statistic: 0.019) and 40–49 years (ΔC-statistic: 0.029) compared to the reference models, respectively.Conclusion:The addition of female-specific factors and psychosocial risk factors improves the discriminatory performance of prediction models for stroke in women under 50.