Predicting kidney injury in critically ill children using advanced decision support tools
Official title: Pediatric Acute Kidney Injury Prediction Using Generalized, Sharable, Usable Decision Support
Funded at University of Rochester · NIH-11003681 · Data updated 2026-11-12
This study is working on a smart tool to help doctors spot when critically ill kids aged 0-11 might be at risk for kidney problems before they happen, so they can take action early and keep them safe.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | January 1, 2024 |
| Project end | December 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $187,611 |
| NIH project number | 5K23DK138299-02 |
| Project ID | NIH-11003681 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are critically ill children aged 0-11 years who are at risk for developing acute kidney injury.
✕Less likely to help
Patients who are not critically ill or do not have risk factors for acute kidney injury may not benefit from this research.
What this research studies
This research focuses on improving the prediction of acute kidney injury (AKI) in critically ill children aged 0-11 years. By utilizing advanced machine learning techniques, the study aims to develop a decision support system that can identify patients at risk for AKI before it occurs, allowing for timely interventions. The approach addresses current limitations in existing prediction models and aims to create a more effective and user-friendly clinical decision support system. This could enhance patient safety and care quality in pediatric intensive care units.
Why it matters
Potential benefit: If successful, this research could lead to earlier identification and prevention of acute kidney injury in children, significantly improving their health outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning for predicting health outcomes, indicating that this approach could be effective in pediatric settings as well.
Where this research is happening
-
University of Rochester
Rochester, United States Active
Researchers
- Principal investigator: Dziorny, Adam C. — University of Rochester
- Study coordinator: Dziorny, Adam C.
Related research
Other NIH-funded projects on the same topics.
-
SEAK Alliance: Improving Care for AKI Patients in the Southeast
A national, patient-centered effort to improve post-hospital care for people who had acute kidney injury in the Southeast.
-
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.
-
Better ways to identify types of sudden kidney injury in U.S. Veterans
Researchers are looking for genetic and health patterns that define different types of sudden severe kidney injury in Veterans to help guide future care.
-
Neuroimmune control of kidney injury using noninvasive ultrasound
A new approach uses targeted ultrasound to gently stimulate nerves and immune signals to help the kidney recover after injury.
-
Structure-based approach to prevent kidney injury from polymyxin use
Aims to block how polymyxin damages the kidney so it can be used more safely to treat hard-to-kill infections.
-
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.