Personalized prevention of AKI after heart surgery using new machine learning methods
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-11370634 · Data updated 2026-11-12
A clinician-scientist fluent in nephrology and critical care is using advanced data mining and machine learning to tailor prevention strategies for kidney injury in patients undergoing cardiac surgery.
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 | 2026 |
| Award (this year) | $156,037 |
| NIH project number | 5K08DK131286-06 |
| Project ID | NIH-11370634 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults undergoing cardiac surgery who are at risk for or have a history of acute kidney injury.
✕Less likely to help
Patients not undergoing cardiac surgery or those without AKI risk factors may derive little to no direct benefit.
What this research studies
This NIH K08 grant supports a career development plan to advance my skills in health informatics, data mining, and machine learning and apply them to prevent acute kidney injury in the cardiac surgery population. I will work with an experienced mentoring team to gain hands-on training and develop independent research leadership in critical care nephrology. The project focuses on personalizing prevention by leveraging complex clinical data to optimize fluid management, blood pressure control, and avoidance of nephrotoxins and hyperglycemia within care bundles. The aim is to translate advanced analytics into practical strategies that reduce AKI incidence and its complications after surgery. The long-term goal is to become a leading investigator who can improve patient outcomes through data-driven, individualized prevention plans.
Why it matters
Potential benefit: If successful, this work could lead to personalized prevention strategies that lower AKI risk and improve recovery after heart surgery.
How similar studies have gone: Previous research has shown that care-bundle interventions can reduce AKI, but integrating personalized machine-learning approaches is still emerging.
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
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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.