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

ABSTRACT:

Importance

Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies.

Objective

To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques.

Design, setting, and participants

Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-

SUBMITTER: Hong C 

PROVIDER: S-EPMC10408266 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature