Current medical standards for accessing stroke risk perform worse for Black Americans than for white Americans, research finds.

The study, published in the Journal of the American Medical Association, evaluated various existing algorithms and two methods of artificial intelligence assessment that are aimed at predicting a person’s risk of stroke within the next 10 years.

The study found that all algorithms were worse at stratifying the risk for Black people than white people, regardless of the person’s gender. The implications are at the individual and population levels: people at high risk of stroke might not receive treatment, and those at low or no risk are unnecessarily treated.

“We need to improve data collection procedures and expand the pool of risk factors for stroke to close the performance gap of algorithms between Black and white adults,” says Michael Pencina, corresponding author of the study, professor in the department of biostatistics and bioinformatics at Duke Health and director of AI Health at Duke University School of Medicine.

“For example, the algorithms tested here mostly do not account for social determinants of health and some other factors suggested by the Stroke Prevention Guideline,” Pencina says. “Data collection needs to be closer to the patient and the community.”

Disparities can potentially become propagated by these algorithms, and things could get worse for some people, which may lead to inequity in treatment decisions for Black versus white adults,” he adds.

Read the full article about stroke risk algorithms by Stephanie Lopez at Futurity.