New methods to predict and prevent kidney stone progression
Official title: Novel evidence-accumulation-driven methods for characterizing kidney stone progression
Funded at Ut Southwestern Medical Center · NIH-11004024 · Data updated 2026-11-12
This study is working on creating personalized tools to help people with kidney stones understand their risk of getting more stones and find ways to prevent them from getting worse, so they can enjoy better health.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ut Southwestern Medical Center NIH-funded |
| Lab location | 1 site (Dallas, United States) |
| Project start | March 1, 2022 |
| Project end | December 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $611,460 |
| NIH project number | 5R01DK128237-04 |
| Project ID | NIH-11004024 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals who have a history of kidney stones or are at risk for developing them.
✕Less likely to help
Patients who have never experienced kidney stones or have conditions unrelated to kidney stone disease may not benefit from this research.
What this research studies
This research aims to improve the prevention of kidney stone progression by developing advanced clinical prediction tools and computational algorithms. It focuses on creating patient-specific risk models that take into account different types of kidney stones and their progression. By analyzing existing data and integrating it with electronic health records, the research seeks to identify modifiable risk factors that can help in preventing recurrent kidney stones. Patients may benefit from personalized strategies that could reduce their risk of developing further complications related to kidney stones.
Why it matters
Potential benefit: If successful, this research could lead to more effective prevention strategies for kidney stone disease, reducing pain and healthcare costs for patients.
How similar studies have gone: Previous research has shown promise in using predictive modeling for chronic diseases, indicating that this approach could be effective for kidney stone progression as well.
Where this research is happening
-
Ut Southwestern Medical Center
Dallas, United States Active
Researchers
- Principal investigator: Liu, Yu-Lun — Ut Southwestern Medical Center
- Study coordinator: Liu, Yu-Lun
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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.