Developing a tool to better understand and treat sepsis using patient data and biomarkers.
Official title: Clinical Validation of a Point-of-Care Precision Medicine Endotyping Platform
Funded at Prenosis, INC. · NIH-11008713 · Data updated 2026-11-12
This study is looking to make sepsis treatment better by using computer technology to group patients based on their unique biological responses, helping doctors understand each person's condition more clearly and improve their care.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Prenosis, INC. NIH-funded |
| Lab location | 1 site (Champaign, United States) |
| Project start | September 30, 2024 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $700,000 |
| NIH project number | 1R44GM154602-01A1 |
| Project ID | NIH-11008713 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with sepsis or those experiencing severe infections requiring acute care.
✕Less likely to help
Patients with non-infectious conditions or those not experiencing acute illness may not benefit from this research.
What this research studies
This research focuses on improving the treatment of sepsis, a complex and varied disease, by using a machine learning approach to classify patients into specific subgroups based on their biological responses. By analyzing blood samples for various protein biomarkers and combining this data with clinical information from electronic medical records, the study aims to create precision medicine maps that reflect each patient's unique condition. This innovative platform will allow healthcare providers to make more informed decisions at the point of care, potentially leading to better outcomes for patients suffering from sepsis.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatments for patients with sepsis.
How similar studies have gone: Previous research has shown promise in using biomarker-based approaches and machine learning for patient classification in sepsis, indicating a potential for success in this novel application.
Where this research is happening
-
Prenosis, INC.
Champaign, United States Active
Researchers
- Principal investigator: Reddy, Bobby — Prenosis, INC.
- Study coordinator: Reddy, Bobby
Related research
Other NIH-funded projects on the same topics.
-
Heart Failure CardioScore: an AI tool to help ED doctors diagnose and predict outcomes for heart failure
A new AI-based tool uses biomarkers and patient data to help doctors diagnose acute heart failure more accurately in the emergency department.
-
Bedside blood test to identify types of sepsis
A quick blood test that helps identify different immune response types in people with sepsis so doctors can choose more tailored care.
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.