New AI approach enhances cardiovascular risk assessment
· News-Medical“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.”
In the study, researchers used electronic health record data from 95,326 Mass General Brigham patients who were 55 or older in 2007 and who had at least one lipid or blood pressure measurement between 1997-2006 and at least one encounter with the hospital system between 2007-2016. The team used XGBoost, an open- source machine learning library, to recalibrate PREVENT’s equations while still preserving the associations of known risk factors with the outcomes observed in the original model. The results demonstrated greater accuracy and the reclassification of one out of ten patients in this population.
Aniket Zinzuwadia, MD, Resident physician, Brigham and Women’s HospitalThis could theoretically represent a group of patients that might not have been prescribed statin therapies in the original application of the model, for example, but who might have benefited from them.”
While more steps are needed before this technique could be applied to patient care, the team would like to see how it performs in the local populations of other healthcare systems and, eventually, for clinicians and researchers to use the tool to tailor global risk models.
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