Using machine learning to predict disease progression and survival in patients with severe limb ischemia.
Machine learning-based staging system to predict chronic limb-threatening ischemia disease progression and survival: A secondary analysis of the BEST-CLI trial data
This study is working on a smart system that helps doctors understand how serious chronic limb-threatening ischemia (CLTI) is for patients and how it changes over time, so they can give the best treatment possible and improve health outcomes.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project ID | NIH-11001221 on NIH RePORTER |
What this research studies
This research aims to develop a machine learning-based system to assess the severity and progression of chronic limb-threatening ischemia (CLTI) in patients. By analyzing data from the BEST-CLI trial, the study will create a comprehensive staging system that evaluates individual disease burden and mortality risk at diagnosis and throughout treatment. This approach seeks to improve patient outcomes by tailoring treatment strategies based on predicted disease progression and survival rates. Patients will be monitored for their response to various interventions, including surgical and medical therapies.
Who could benefit from this research
Good fit: Ideal candidates for this research are adults diagnosed with chronic limb-threatening ischemia who are undergoing treatment for this condition.
Not a fit: Patients who do not have chronic limb-threatening ischemia or those who are not receiving treatment for this condition may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more personalized treatment plans that significantly improve survival rates and quality of life for patients with CLTI.
How similar studies have performed: Previous studies have shown promise in using machine learning for predicting outcomes in various medical conditions, suggesting potential success for this novel approach in CLTI.
Where this research is happening
Chapel Hill, United States
- Univ of North Carolina Chapel Hill — Chapel Hill, United States (Active)
Researchers
- Principal investigator: Mcginigle, Katharine L — Univ of North Carolina Chapel Hill
- Study coordinator: Mcginigle, Katharine L
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 below.