Using machine learning to predict kidney disease progression in sickle cell anemia
Official title: Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models (PREMIER)
Funded at University of Tennessee Health Sci Ctr · NIH-10903806 · Data updated 2026-11-12
This study is looking at how computer models can help predict kidney problems in people with sickle cell anemia by checking things like protein levels in urine and genetic information, so we can find those who might need extra care early on to keep their kidneys healthy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Tennessee Health Sci Ctr NIH-funded |
| Lab location | 1 site (Memphis, United States) |
| Project start | September 10, 2021 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $606,572 |
| NIH project number | 5R01HL159376-04 |
| Project ID | NIH-10903806 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals aged 21 and older with sickle cell anemia who are at risk for chronic kidney disease.
✕Less likely to help
Patients without sickle cell anemia or those who do not have chronic kidney disease may not benefit from this research.
What this research studies
This research investigates how machine learning models can be utilized to predict the progression of chronic kidney disease (CKD) in patients with sickle cell anemia. By analyzing factors such as albuminuria and genetic variants, the study aims to identify individuals at high risk for rapid kidney function decline. The goal is to enhance early detection and management of CKD, ultimately improving patient outcomes. Participants may undergo assessments that help determine their risk levels and guide treatment options.
Why it matters
Potential benefit: If successful, this research could lead to better management strategies for kidney disease in sickle cell anemia patients, potentially reducing complications and improving quality of life.
How similar studies have gone: Previous research has shown promise in using machine learning to identify high-risk patients in similar contexts, indicating a potential for success in this approach.
Where this research is happening
-
University of Tennessee Health Sci Ctr
Memphis, United States Active
Researchers
- Principal investigator: Ataga, Kenneth I — University of Tennessee Health Sci Ctr
- Study coordinator: Ataga, Kenneth I
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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.