Key result
Gender-specific stroke risk prediction models incorporating age, smoking, blood pressure, albuminuria, and diabetes demonstrated good discrimination with C-statistics of 0.761 for women and 0.765 for men in an American Indian population.
Cohort (n=3,483)
Yes
A newly developed stroke risk prediction model tailored for American Indians demonstrated good discrimination and calibration, providing a population-specific tool for stroke risk assessment.
Answers are grounded in this paper and cite their sources.
May support tailored stroke risk assessment in American Indians; leaves open external validation before clinical adoption.
BACKGROUND AND OBJECTIVE: American Indians have a high prevalence of diabetes and higher incidence of stroke than that of whites and blacks in the U.S. Stroke risk prediction models based on data from American Indians would be of clinical and public health value. METHODS AND RESULTS: A total of 3483 (2043 women) Strong Heart Study participants free of stroke at baseline were followed from 1989 to 2010 for incident stroke. Overall, 297 stroke cases (179 women) were identified. Cox models with stroke-free time and risk factors recorded at baseline were used to develop stroke risk prediction models. Assessment of the developed stroke risk prediction models regarding discrimination and calibration was performed by an analogous C-statistic (C) and a version of the Hosmer-Lemeshow statistic (HL), respectively, and validated internally through use of Bootstrapping methods. RESULTS: Age, smoking status, alcohol consumption, waist circumference, hypertension status, an-tihypertensive therapy, fasting plasma glucose, diabetes medications, high/low density lipoproteins, urinary albumin/creatinine ratio, history of coronary heart disease/heart failure, atrial fibrillation, or Left ventricular hypertrophy, and parental history of stroke were identified as the significant optimal risk factors for incident stroke. DISCUSSION: The models produced a C = 0.761 and HL = 4.668 (p = 0.792) for women, and a C = 0.765 and HL = 9.171 (p = 0.328) for men, showing good discrimination and calibration. CONCLUSIONS: Our stroke risk prediction models provide a mechanism for stroke risk assessment designed for American Indians. The models may be also useful to other populations with high prevalence of obesity and/or diabetes for screening individuals for risk of incident stroke and designing prevention programs.
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Wang et al. (2017) conducted a cohort in Stroke risk prediction (n=3,483). Multiple cardiovascular risk factors (including albuminuria and diabetes) vs. Baseline risk was evaluated on Incident stroke prediction (C-statistic) (C-statistic 0.761 (women) and 0.765 (men)). Gender-specific stroke risk prediction models incorporating age, smoking, blood pressure, albuminuria, and diabetes demonstrated good discrimination with C-statistics of 0.761 for women and 0.765 for men in an American Indian population.
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