Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

CONCLUSIONS AND RELEVANCE: 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.PMID:36692561 | DOI:10.1001/jama.2022.24683

Source: Atherosclerosis - January 24, 2023 Category: Cardiology Authors: Chuan Hong Michael J Pencina Daniel M Wojdyla Jennifer L Hall Suzanne E Judd Michael Cary Matthew M Engelhard Samuel Berchuck Ying Xian Ralph D'Agostino George Howard Brett Kissela Ricardo Henao Source Type: research