Added predictive value of female-specific factors and psychosocial factors for the risk of stroke in women under 50

Os, H.J.A. van; Kanning, J.P.; Ferrari, M.D.; Bonten, T.N.; Kist, J.M.; Vos, H.M.M.; Vos, R.C.; Putter, H.; Groenwold, R.H.H.; Wermer, M.J.H.

doi:doi:10.1212/WNL.0000000000207513

Background and Objectives
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 younger than 50 years.
Methods
We used data from the Stichting Informatievoorziening voor Zorg en Onderzoek, 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 slope of the calibration curve at a
follow-up of 10 years. We developed an age...Show moreBackground and Objectives
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 younger than 50 years.
Methods
We used data from the Stichting Informatievoorziening voor Zorg en Onderzoek, 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 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); and 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 discrimi-
nation increased moderately, especially in the age groups 30–39 (Dc-statistic: 0.019) and 40–49
years (Dc-statistic: 0.029) compared with the reference models, respectively.
Discussion
The addition of female-specific factors and psychosocial risk factors improves the discrimina-
tory performance of prediction models for stroke in women younger than 50 years.
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Added predictive value of female-specific factors and psychosocial factors for the risk of stroke in women under 50