AI-powered ECG tool to quickly detect and predict ventricular tachycardia during in-hospital cardiac arrest
Official title: Evaluation of a novel ECG algorithm to identify and predict ventricular tachycardia in ICU and non-ICU patients with in-hospital cardiac arrest
Funded at University of California, San Francisco · NIH-11263717 · Data updated 2026-11-12
A new AI-enhanced ECG algorithm is being developed to rapidly identify and forecast dangerous ventricular rhythms in adults with in-hospital cardiac arrest, aiming to improve defibrillation timing.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | February 1, 2024 |
| Project end | January 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $821,296 |
| NIH project number | 5R01HL167975-03 |
| Project ID | NIH-11263717 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults 21 years or older who experience or are at risk for in-hospital cardiac arrest and whose data can be used to train or validate the algorithm.
✕Less likely to help
Patients whose IHCA does not involve VT or where ECG data cannot be captured may not benefit directly.
What this research studies
This project aims to reduce false alarms and speed up correct VT detection during IHCA by using an advanced AI-enabled ECG monitoring system. Researchers will work with real patient ECG data to design and test an algorithm that can identify VT patterns and predict when a shockable rhythm may occur. The study will compare the AI tool’s performance against current monitoring standards in ICU and non-ICU settings, with attention to speed, accuracy, and usability for clinicians. The ultimate goal is to help clinicians deliver timely defibrillation and improve survival with better neurological outcomes.
Why it matters
Potential benefit: If successful, the tool could shorten response times and reduce unnecessary alarms, potentially saving lives and preserving brain function after IHCA.
How similar studies have gone: Previous work suggests AI-based ECG analysis can improve rhythm detection, but false alarms remain a major challenge, making this project innovative and potentially more effective.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Pelter, Michele M. — University of California, San Francisco
- Study coordinator: Pelter, Michele M.
Related research
Other NIH-funded projects on the same topics.
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New ECG algorithm to detect dangerous heart rhythms in hospitalized patients
This study is working on a new way to better spot a dangerous heart rhythm called ventricular tachycardia in patients who have heart emergencies in the hospital, so doctors can respond quickly and accurately without being distracted by false alarms.
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Map heart tissue to guide VT ablation with AI help
A patient-oriented project aims to use AI-based mapping to better identify abnormal heart tissue responsible for ventricular tachycardia and guide safer, faster ablation.
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Proton SBRT for refractory ventricular tachycardia with real-time 3D/4D imaging
A patient-facing study exploring a proton-based, noninvasive radiation therapy to treat stubborn VT using advanced 3D and 4D imaging to target the heart safely.
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Venous ethanol ablation to treat ischemic VT (VE-VT)
A new treatment approach uses ethanol delivered through heart veins to help stop dangerous heart rhythms in patients with previous heart damage.
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Novel gene therapy tools to prevent dangerous heart rhythms
A small grant supports testing gene-therapy methods to stop ventricular tachycardia after a heart attack, with the goal of helping people at risk of sudden heart rhythm problems.
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Radiation-guided ablation for ventricular tachycardia
This project tests a noninvasive radiation approach to ablate heart tissue in people with dangerous ventricular tachycardia to reduce the need for invasive procedures.
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.