Predicting kidney injury risks after surgery using AI
Official title: Real-time Prediction of Adverse Outcomes After Surgery
Funded at University of California, San Francisco · NIH-10873873 · Data updated 2026-11-12
This study is working on using smart computer technology to help doctors spot patients who might be at risk for kidney problems during surgery, so they can take action to keep everyone safe.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | August 1, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $188,332 |
| NIH project number | 5K23GM151611-02 |
| Project ID | NIH-10873873 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults undergoing surgical procedures who may be at risk for acute kidney injury.
✕Less likely to help
Patients who are not undergoing surgery or those with pre-existing severe kidney conditions may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning and artificial intelligence techniques to predict the risk of acute kidney injury (AKI) in patients undergoing surgery. By analyzing various data points and patient characteristics in real-time, the goal is to identify individuals at higher risk for adverse outcomes, allowing for timely interventions. The project involves collaboration with experts in data science and kidney health to enhance the predictive models and improve patient safety during the perioperative period.
Why it matters
Potential benefit: If successful, this research could significantly reduce the incidence of acute kidney injury in surgical patients, leading to better recovery outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning for predicting surgical outcomes, indicating that this approach could be effective.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Bishara, Andrew — University of California, San Francisco
- Study coordinator: Bishara, Andrew
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.