Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.

Background: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. Although clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high versus low risk of developing aggressive CAV. This investigation aimed to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMBs) to develop a precision medicine tool for predicting CAV years before overt clinical presentation.