Using AI to monitor lung fluid in sudden (acute) heart failure in the ER
An AI-Assisted Strategy for Monitoring Pulmonary Congestion in Acute Heart Failure Patients in Emergency Settings
This project will use an AI-assisted lung ultrasound method to track lung congestion in people who come to the emergency department with acute heart failure.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham and Women's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-11267990 on NIH RePORTER |
What this research studies
If you come to the ER with sudden worsening heart failure and shortness of breath, clinicians would get quick bedside lung ultrasound images while you are being treated. An AI tool would help read those images so doctors can track how much fluid is in your lungs day-to-day without waiting for slower tests. Care teams could use those real-time results to guide diuretics and other treatments to help relieve congestion before discharge. The work builds on earlier clinical studies showing lung ultrasound finds changes in congestion faster than usual measures and aims to remove the need for a specialized human reader.
Who could benefit from this research
Good fit: Adults who present to the emergency department with symptoms of acute heart failure and suspected pulmonary congestion (shortness of breath, fluid overload) would be the intended participants.
Not a fit: People without heart failure, those with chronic stable heart failure not seeking acute care, children, or patients unable to undergo lung ultrasound likely would not benefit from participation.
Why it matters
Potential benefit: If successful, this could help clinicians find and remove lung fluid faster, lowering the chance of readmission or short-term death after an ER visit for acute heart failure.
How similar studies have performed: Prior clinical work (BLUSHED-AHF and CARVD-AHF) showed daily lung ultrasound captures congestion changes faster and can guide therapy, while the AI automation approach is a newer, building step.
Where this research is happening
Boston, United States
- Brigham and Women's Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Kapur, Tina — Brigham and Women's Hospital
- Study coordinator: Kapur, Tina
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.