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Patients at Risk of Dying Post-Surgery Identified Pre-Op

Study: Immunocompromised Response to COVID-19 Vaccination

Researchers develop data tool that may improve care

 E-Mail IMAGE: Medical director of high-risk populations and outcomes, UPMC Wolff Center; associate professor of surgery, University of Pittsburgh School of Medicine. view more  Credit: UPMC PITTSBURGH, Feb. 8, 2021 - With the aid of sophisticated machine learning, researchers at UPMC and the University of Pittsburgh School of Medicine demonstrated that a tool they developed can rapidly predict mortality for patients facing transfer between hospitals in order to access higher-acuity care. This research, published today in PLOS One, could help physicians, patients and their families avoid unnecessary hospital transfers and low-value treatments, while better focusing on the goals of care expressed by patients.