Using artificial intelligence to predict outcomes for patients with acute kidney injury on dialysis
Official title: Artificial Intelligence to Predict Outcomes in Patients with Acute Kidney Injury on Continuous Renal Replacement Therapy
Funded at University of Alabama at Birmingham · NIH-11018619 · Data updated 2026-11-12
This study is looking to help doctors better predict how critically ill patients with acute kidney injury might recover while they are on a special kidney treatment, using advanced technology to personalize their care and improve their chances of getting better.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Alabama at Birmingham NIH-funded |
| Lab location | 1 site (Birmingham, United States) |
| Project start | April 1, 2023 |
| Project end | January 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $609,234 |
| NIH project number | 5R01DK133539-03 |
| Project ID | NIH-11018619 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are critically ill patients admitted to intensive care units who are experiencing acute kidney injury and require continuous renal replacement therapy.
✕Less likely to help
Patients who do not have acute kidney injury or those who are not critically ill may not benefit from this research.
What this research studies
This research focuses on improving the prediction of survival and kidney recovery in critically ill patients suffering from acute kidney injury (AKI) who are undergoing continuous renal replacement therapy (CRRT). By utilizing advanced artificial intelligence techniques, particularly deep learning, the study aims to develop accurate risk prediction models that can guide clinical decision-making and personalize care for these patients. The research will also explore patient sub-phenotypes to identify those at varying risks of recovery, ultimately enhancing treatment strategies. The goal is to provide timely and actionable insights that can improve patient outcomes in intensive care settings.
Why it matters
Potential benefit: If successful, this research could lead to better prediction of recovery outcomes for patients with acute kidney injury, allowing for more tailored and effective treatment plans.
How similar studies have gone: Other research has shown promise in using artificial intelligence for predicting patient outcomes in critical care settings, indicating that this approach could be effective.
Where this research is happening
-
University of Alabama at Birmingham
Birmingham, United States Active
Researchers
- Principal investigator: Neyra, Javier a. — University of Alabama at Birmingham
- Study coordinator: Neyra, Javier a.
Related research
Other NIH-funded projects on the same topics.
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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.