Survivorship-based model to improve liver transplant waiting lists
Official title: SURVIBENE-LT: A Survivorship-Based Benefit Model for Optimized Liver Transplantation Allocation
Funded at University of Florida · NIH-11365694 · Data updated 2026-11-12
A data-driven model using AI will help allocate liver transplants more fairly and effectively, aiming to reduce deaths on the waitlist and improve long-term outcomes for patients with end-stage liver disease.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Florida NIH-funded |
| Lab location | 1 site (Gainesville, United States) |
| Project start | July 15, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $745,946 |
| NIH project number | 1R01DK142809-01A1 |
| Project ID | NIH-11365694 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with end-stage liver disease who are candidates for liver transplantation and are on or eligible for waitlisting.
✕Less likely to help
Patients not on transplant waitlists or those not facing liver failure may not directly benefit.
What this research studies
This project seeks to create a personalized, objective tool that predicts wait times and balances pre- and post-transplant benefits. It will use patient data to guide prioritization beyond current MELD-based methods, focusing on reducing waitlist mortality and improving post-transplant survival. The team will validate the model with real-world data and explore how transparency and consistency across centers can be improved for patients. Overall, the work aims to make liver transplantation decisions more fair, precise, and outcomes-driven from the patient perspective.
Why it matters
Potential benefit: If successful, patients could experience shorter, more equitable wait times and better chances of surviving and thriving after transplantation.
How similar studies have gone: Previous models improved prioritization for some aspects but often lacked full transparency and post-transplant outcome optimization; this project seeks to extend those gains with a more integrated, data-driven approach.
Where this research is happening
-
University of Florida
Gainesville, United States Active
Researchers
- Principal investigator: Pruinelli, Lisiane — University of Florida
- Study coordinator: Pruinelli, Lisiane
Related research
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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.