PI: Natalia Trayanova (Biomedical Engineering and Medicine)

Co-I: Allison Hayes (Cardiology)

It is now recognized that patients recovered from COVID-19, especially those with severe COVID requiring intensive care, frequently develop long-term debilitating symptoms and hospital readmissions. Although acute cardiac complications due to COVID-19 are now described, the long-term cardiovascular (CV) complications of COVID remain unclear. It is not known what is the frequency and nature of the CV complications, or what are the predictors for developing such adverse events in the long term posthospitalization. We are now in a unique position to address this pressing clinical need. The goal of this project is to develop a real-time machine learning (ML) solution to predict long-term (1 year) adverse CV events in patients who were discharged after hospital admission for COVID-19. The warning system will be able to identify at-risk patients in real time and alert caregivers and patients, reducing mortality, ensuring the delivery of goal-oriented therapy, and providing tangible clinical decision support.