Identifying different types of critical illness in patients with acute respiratory distress syndrome and sepsis
Official title: Clinical Implementation of Molecular Phenotypes of Critical Illness
Funded at Washington University · NIH-11050900 · Data updated 2026-11-12
This study is looking at how to better understand and treat serious conditions like acute respiratory distress syndrome and sepsis by using smart technology to sort patients based on their unique symptoms, which could help doctors provide more personalized care right in the hospital.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Washington University NIH-funded |
| Lab location | 1 site (Saint Louis, United States) |
| Project start | January 28, 2025 |
| Project end | November 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $737,815 |
| NIH project number | 1R01HL173531-01A1 |
| Project ID | NIH-11050900 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults diagnosed with acute respiratory distress syndrome or sepsis.
✕Less likely to help
Patients with chronic respiratory conditions or those not experiencing acute respiratory distress may not benefit from this research.
What this research studies
This research focuses on understanding the different molecular phenotypes of acute respiratory distress syndrome (ARDS) and sepsis, which are serious conditions often seen in critical care. By using advanced machine learning techniques, the study aims to develop a system that can classify patients based on their specific illness characteristics using easily obtainable clinical data like vital signs and lab results. This classification could help tailor treatments more effectively and improve patient outcomes. The goal is to create a tool that can be integrated into electronic health records for real-time use in hospitals.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatments for patients suffering from ARDS and sepsis.
How similar studies have gone: Previous research has shown promise in using machine learning for patient classification in critical care, indicating potential for success in this novel approach.
Where this research is happening
-
Washington University
Saint Louis, United States Active
Researchers
- Principal investigator: Sinha, Pratik — Washington University
- Study coordinator: Sinha, Pratik
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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.