Donor lung CT images to improve predicting severe graft dysfunction after lung transplant
Official title: Incorporating Donor Lung CT Images into Machine Learning Models to Predict Severe Primary Graft Dysfunction after Lung Transplantation
Funded at Utah State Higher Education System--University of Utah · NIH-11394262 · Data updated 2026-11-12
A study aims to use donor lung CT scans together with clinical information to better predict which patients are at high risk of severe graft dysfunction after lung transplantation.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Utah State Higher Education System--University of Utah NIH-funded |
| Lab location | 1 site (Salt Lake City, United States) |
| Project start | August 15, 2025 |
| Project end | May 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $758,611 |
| NIH project number | 5R01HL177046-02 |
| Project ID | NIH-11394262 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are adults receiving lung transplants from donor lungs whose imaging data and clinical records are available at participating centers.
✕Less likely to help
Patients whose donor CT data or clinical records are unavailable or whose PGD risk is not influenced by imaging factors may see limited benefit.
What this research studies
This project will collect retrospective data from five US lung transplant centers to build an advanced model that combines donor CT images with clinical factors from the donor and recipient. Researchers will harmonize imaging and clinical data, then train machine learning algorithms to predict severe primary graft dysfunction within the first 72 hours after transplant. The goal is to provide more accurate risk estimates to guide donor selection and posttransplant care. Results could improve early interventions and long-term outcomes for transplant patients.
Why it matters
Potential benefit: If successful, the model could help clinicians identify high-risk cases earlier and tailor posttransplant care to reduce complications.
How similar studies have gone: Previous work used clinical data alone; this study adds donor CT imaging to potentially improve prediction performance.
Where this research is happening
-
Utah State Higher Education System--University of Utah
Salt Lake City, United States Active
Researchers
- Principal investigator: Hachem, Ramsey — Utah State Higher Education System--University of Utah
- Study coordinator: Hachem, Ramsey
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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.