Award
NIH Reporter #5K23GM151611-04
Real-time Prediction of Adverse Outcomes After Surgery
Recipient
University of California, San Francisco
Award Amount
$188,332.00
Ceiling
$188,332.00
Awarded
July 29, 2026
Identifier
5K23GM151611-04
Development and implementation of machine learning models for real-time prediction of perioperative acute kidney injury to improve clinical decision-making and patient outcomes.
Description
This project aims to develop and implement machine learning models to predict perioperative acute kidney injury (AKI) in real-time during surgery. Despite effective kidney-protective strategies, AKI remains a significant complication affecting 18-47% of surgical patients, leading to adverse outcomes such as chronic kidney disease, cardiovascular events, increased healthcare costs, and death. The project proposes creating innovative visualization technology to enhance provider interaction with predictive models, enabling timely interventions to prevent AKI.