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Tool Predicts Sudden Death in Inflammatory Heart Disease Johns Hopkins University scientists have developed a new tool for predicting which patients suffering from a complex inflammatory heart disease are at risk of sudden cardiac arrest. Published in  Science Advances, their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning. “This robust new personalized technology outperformed clinical metrics in forecasting future arrhythmia and could transform the management of cardiac sarcoidosis patients,” said senior author Natalia Trayanova, a Johns Hopkins professor of biomedical engineering and co-director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE).

Rebecca yuDavidr okadaNatalia trayanovaJonathan chrispinAdityo prakosaDanm popescuJohns hopkinsRoberte meyerhoffJohns hopkins universityLowenstein foundationJohns hopkins school of medicineJohns hopkins university school of medicineAlliance for cardiovascularResearch fellowshipJohns hopkins university undergraduate research fellowshipScience advances