Using machine learning to prevent kidney injury after heart surgery
Official title: Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac Surgery
Funded at Icahn School of Medicine at Mount Sinai · NIH-10894799 · Data updated 2026-11-12
This study is looking to improve care for patients who might develop kidney problems after heart surgery by using smart computer techniques to create personalized plans that help manage their fluids and blood pressure better.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | January 4, 2024 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $158,842 |
| NIH project number | 5K08DK131286-04 |
| Project ID | NIH-10894799 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients scheduled for cardiac surgery who are at risk for developing acute kidney injury.
✕Less likely to help
Patients who are not undergoing cardiac surgery or those with pre-existing severe kidney disease may not benefit from this research.
What this research studies
This research aims to enhance the care of patients at risk for acute kidney injury (AKI) following cardiac surgery by utilizing advanced machine learning techniques. The project focuses on developing personalized prevention strategies that optimize patient management, including fluid status and blood pressure control. By analyzing data from patients undergoing cardiac procedures, the research seeks to identify effective interventions that can reduce the incidence of AKI. The principal investigator, trained in both nephrology and critical care, is committed to integrating health informatics and data science into clinical practice.
Why it matters
Potential benefit: If successful, this research could significantly reduce the occurrence of acute kidney injury in patients undergoing cardiac surgery, leading to better overall health outcomes.
How similar studies have gone: Previous research has shown promise in using data-driven approaches to manage acute kidney injury, indicating that this method could lead to meaningful advancements in patient care.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Sakhuja, Ankit — Icahn School of Medicine at Mount Sinai
- Study coordinator: Sakhuja, Ankit
Related research
Other NIH-funded projects on the same topics.
-
AI-powered system to improve teamwork in cardiac surgery
A research project is testing an AI-driven system that listens to team conversations during heart surgeries to spot teamwork patterns and give real-time feedback to improve patient safety.
-
AKI-related delirium after heart surgery
This grant funds research to understand why some people develop delirium after heart surgery when they also have acute kidney injury.
-
Predictors of widespread pain after cardiac surgery
A clinician-scientist at UNC Chapel Hill is studying why some patients develop widespread, nociplastic-like pain after heart surgery and ICU stay, to improve prevention and care.
-
Statistical methods to link high-dimensional heart-surgery data with outcomes
This grant tests advanced data methods to find links between detailed intraoperative heart-surgery measurements and patient outcomes after procedures like CABG.
-
Wearable ultrasound patch to monitor heart health after pediatric heart surgery
A small, disposable ultrasound patch worn on babies and young children after heart surgery will measure heart function and blood pressure in real time, aiming to catch problems early.
-
Using smart computer predictions to prevent kidney injury after heart surgery
A machine-learning approach uses routinely collected hospital data to predict who might develop acute kidney injury after heart surgery, so clinicians can act early.
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.