Stroke risk prediction tools are meant to guide how doctors approach a potentially deadly condition, using factors like heart disease and high blood pressure to get a handle on which patients might benefit from a particular treatment.
For years, doctors have used several different algorithms to try to capture the true risk of stroke, including newer models that use machine learning. A new analysis, led by researchers at Duke University School of Medicine, compared several of those algorithms head-to-head — and found that novel machine learning models weren’t much more accurate at predicting the risk of stroke than simpler algorithms based on self-reported risk factors and an older methodology. Alarmingly, the study also found all the algorithms were worse at stratifying risk for Black men and women than for white.
“We got the shocking result — to me, shocking — that the measure of discrimination, the ability to rank them, was much better… for white participants than Black participants,” said Michael Pencina, director of Duke AI Health and one of the lead researchers on the study.
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