# Real-time Prediction of Adverse Outcomes After Surgery

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $188,332

## Abstract

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 st...

## Key facts

- **NIH application ID:** 10724048
- **Project number:** 1K23GM151611-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Andrew Bishara
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $188,332
- **Award type:** 1
- **Project period:** 2023-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10724048

## Citation

> US National Institutes of Health, RePORTER application 10724048, Real-time Prediction of Adverse Outcomes After Surgery (1K23GM151611-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10724048. Licensed CC0.

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