Risk calculators are used to evaluate disease risk for millions of patients, making their accuracy crucial. But when national models are adapted for local populations, they often deteriorate, losing accuracy and interpretability. Investigators from Brigham and Women's Hospital, a founding member of the Mass General Brigham health care system, used advanced machine learning to increase the accuracy of a national cardiovascular risk calculator while preserving its interpretability and original risk associations.
Their results showed higher accuracy overall in an electronic health records cohort from Mass General Brigham and reclassified roughly one in 10 patients into a different risk category to facilitate more precise treatment decisions. The results are published in JAMA Cardiology. "Risk calculators are incredibly important as they are an integral part of the conversation between providers and patients on risk prevention," said first author Aniket Zinzuwadia, MD, a resident physician in Internal Medicine at Brigham and Women's Hospital.
"But sometimes, when applying these global calculators to local populations , there is variability inherent to the nature of an area—whether that is different demographic characteristics, different physician practice patterns, or different risk factors—so we wanted to find a way to tailor the foundational cardiovascular disease risk model to local populations in a safe way that builds upon what is already being done." The American Heart .