Do existing stroke-specific risk prediction models and novel machine learning techniques improve discriminative accuracy for new-onset stroke compared to pooled cohort equations in Black and White individuals without prior stroke or TIA?
Current stroke risk prediction models do not outperform standard pooled cohort equations and demonstrate worse discrimination in Black individuals, highlighting a need for improved modeling to address racial disparities.
In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.
Hong et al. (Tue,) studied this question.
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