Predicting kidney injury risks after surgery using AI
Real-time Prediction of Adverse Outcomes After Surgery
['FUNDING_OTHER'] · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · NIH-10873873
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
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF CALIFORNIA, SAN FRANCISCO (nih funded) |
| Locations | 1 site (SAN FRANCISCO, UNITED STATES) |
| Trial ID | NIH-10873873 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research are adults undergoing surgical procedures who may be at risk for acute kidney injury.
Not a fit: Patients who are not undergoing surgery or those with pre-existing severe kidney conditions may not benefit from this research.
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 performed: 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
SAN FRANCISCO, UNITED STATES
- UNIVERSITY OF CALIFORNIA, SAN FRANCISCO — SAN FRANCISCO, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: BISHARA, ANDREW — UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- Study coordinator: BISHARA, ANDREW
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 below.