New machine-learning model can identify individuals at risk of rare cardiomyopathy

A machine-learning model can identify patients at risk of a rare cardiomyopathy, according to a study published in Nature Communications. Transthyretin amyloid cardiomyopathy (ATTR-CM) can cause heart failure and should be treated differently than other causes of heart failure, so diagnosis is key, according to Sanjiv Shah, '00 MD, the Neil J. Stone, MD, Professor, director of the Center for Deep Phenotyping and Precision Therapeutics at the Institute for Augmented Intelligence in Medicine and senior author of the study. "If we can flag patients in EMR and trigger clinicians to order screening tests, we can diagnose ATTR-CM earlier and get it treated more quickly," said Shah, who is also a professor of Medicine in the Division of Cardiology. ATTR-CM is caused by defects in transthyretin, one of the most common proteins in the body. Normally, transthyretin is present in a tetramer -- a set of four proteins bound together -- and the complexes help transport hormones and vitamins throughout the body. ATTR-CM is underdiagnosed, according to Shah, so in the current study, the investigators analyzed a large database of medical claims data to develop a machine learning model to identify ATTR-CM from electronic medical records.