Using machine learning to assess and manage the risk of blood clots in hospitalized patients.
Official title: VTE Risk Assessment and Management: Machine Learning from Clinical and Genetic Information
Funded at Baltimore VA Medical Center · NIH-11052800 · Data updated 2026-11-12
This study is working on a new tool to help doctors better prevent blood clots in hospitalized Veterans by using smart technology to understand each patient's risk and preferences, making sure they get the best care possible.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Baltimore VA Medical Center NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project start | April 1, 2025 |
| Project end | March 31, 2029 |
| Funding year | 2025 |
| NIH project number | 1I01CX002747-01A1 |
| Project ID | NIH-11052800 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are hospitalized Veterans who are at risk for venous thromboembolism.
✕Less likely to help
Patients who are not hospitalized or those who do not have risk factors for venous thromboembolism may not benefit from this research.
What this research studies
This research focuses on improving the prevention of venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, particularly in hospitalized Veterans. By utilizing machine learning techniques, the project aims to develop a clinical decision support tool that evaluates individual patient risk for VTE and the potential benefits and risks of various prophylaxis strategies. The tool will also consider patient preferences regarding the prevention of VTE versus the risk of bleeding from prophylactic treatments. This approach seeks to enhance the accuracy of risk assessments and improve patient outcomes in a hospital setting.
Why it matters
Potential benefit: If successful, this research could significantly reduce the incidence of preventable blood clots in hospitalized patients, leading to fewer complications and deaths.
How similar studies have gone: Previous research has indicated that machine learning approaches can improve risk assessment in various medical contexts, suggesting potential success for this novel application in VTE prevention.
Where this research is happening
-
Baltimore VA Medical Center
Baltimore, United States Active
Researchers
- Principal investigator: Lal, Brajesh K — Baltimore VA Medical Center
- Study coordinator: Lal, Brajesh K
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.