Stroke is a leading cause of death and serious long-term disability in the US, with a particularly high risk among Black individuals. Risk prediction tools help identify who would benefit most from prevention, but they need to work equally well for everyone. This study compared several of these tools, including two stroke-specific models, a general cardiovascular risk model and two new machine learning models, using data from 62,482 Black and White adults aged 45 and over from four large US studies.
Overall, the models performed similarly, and the new machine learning models did not improve their accuracy. However, all of them were worse at distinguishing between people who would and would not have a stroke when applied to Black individuals. Among women, the models’ accuracy score was 0.76 for White women and 0.69 for Black women. Among men, it ranged from 0.71 to 0.72 for White men and from 0.64 to 0.66 for Black men. The models also performed better in adults under 60 than in older adults.
The authors conclude that current approaches are not enough to close this gap, and that the range of risk factors considered and the modelling techniques used need to be improved to address these racial disparities.
Learn more about this study here: https://doi.org/10.1001/jama.2022.24683
Reference:
Hong, C., Pencina, M.J., Wojdyla, D.M., Hall, J.L., Judd, S.E., Cary, M., Engelhard, M.M., Berchuck, S., Xian, Y., D’Agostino, R., Howard, G., Kissela, B., & Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. JAMA, 329(4), 306–317.