Real-time Prediction of Adverse Outcomes After Surgery

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The goal of this K23 application is to provide Dr. Bishara with the necessary research experience and time to establish himself as a primary investigator focused on designing and implementing machine learning (ML) and artificial intelligence in the perioperative setting. The career development activities in this application include early intensive course work in ML and statistics focused on improving model development and causal inference techniques. Then coursework focuses on clinical trial training, grantsmanship, responsible conduct of research, and culminates in a course studying implementation science and algorithmic human-robot interaction. Augmenting this training are project-specific tutorials with experts to improve the models proposed in this application with a focus on real-time prediction of perioperative acute kidney injury (AKI) and describing the risk landscape of perioperative AKI. To achieve these goals, Dr. Bishara has assembled a team of experts and mentors in the areas of data science, AKI, ML, and statistics. Dr. Atul Butte, his primary mentor, is an expert in data science and ML and has trained nearly 100 post-doctoral fellows, undergraduate and graduate students, and staff. Dr. Kathleen Liu is a thought leader in the field of AKI with an active research program focused on AKI and critical care clinical trials. She has mentored numerous junior faculty, including previous NIH K23 awardees. Dr. Romain Pirracchio is an expert in biostatistics and ML in acute care. He has collaborations with Berkeley and the FDA and over 100 publications in the realm. These three mentors and the impressive team of advisors will guide Dr. Bishara to complete the project described below and to grow into an independent investigator. There has been a recent surge in the published literature on ML in medicine, and studies have shown patient care improves when provider expertise is augmented by ML. Unfortunately, implementing published ML models to inform clinical care is not trivial, as many obstacles exist. This application focuses on exploring and overcoming those obstacles by implementing specific models in the perioperative setting. Dr. Bishara has developed novel ML visualization technology that allows for improved interactions between providers and models, which provide predictions and recommendations to those providers. This technology also allows for improved regular monitoring and interpretation of the model to assure sustained accuracy and reliability. He will apply this new technology to predict perioperative AKI in real-time, building upon models he has developed. Postoperative AKI is a major public health problem affecting up to 47% of patients and is consistently associated with adverse outcomes, including, major adverse cardiovascular events (MACE), increased healthcare costs, and death. Randomized controlled trials show that implementation of kidney-protective strategies prevents AKI for high-risk patients. Evidence suggests these strategies are underutilized as risk of AKI is often underestimated in the perioperative setting. Dr. Bishara hypothesizes his ML models will identify those patients who will most benefit from timely kidney protective interventions at the time when preventative strategies can be initiated.