Computer modeling to improve heart pacing for ischemic heart failure
Official title: Mathematical Model-Based Optimization of CRT Response in Ischemia
Funded at California Medical Innovations Institute · NIH-11127384 · Data updated 2026-11-12
This project uses 3-D computer models and machine learning to find better pacing strategies for people with heart failure and conduction problems after ischemia.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | California Medical Innovations Institute NIH-funded |
| Lab location | 1 site (San Diego, United States) |
| Project start | July 1, 2023 |
| Project end | June 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $771,198 |
| NIH project number | 5R01HL166508-03 |
| Project ID | NIH-11127384 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with heart failure who are candidates for cardiac resynchronization therapy or who have bundle branch block or other intraventricular conduction delays, especially after ischemic heart damage, are the most relevant group.
✕Less likely to help
People without heart failure, without conduction block, or those not eligible for device-based pacing are unlikely to benefit directly from this project.
What this research studies
Researchers will build detailed 3-D, physics-based computer models of hearts that include scars from ischemia and conduction delays. They will combine those simulations with machine learning algorithms to predict how different pacing approaches, including traditional CRT and newer conduction system pacing, change heart activation and function. The team plans to explore short- and long-term effects of pacing in hearts with varying patterns of scar and intraventricular conduction delay. Results are intended to guide more personalized pacing choices and to point to approaches to raise the fraction of patients who benefit from therapy.
Why it matters
Potential benefit: If successful, this work could increase the number of patients who respond to cardiac pacing and help tailor pacing to each person's pattern of scar and conduction delay.
How similar studies have gone: Prior computational and clinical work shows CRT can help many patients and computer models have been useful in predicting pacing effects, but combining multiscale physics models with machine learning to personalize CRT is relatively new.
Where this research is happening
-
California Medical Innovations Institute
San Diego, United States Active
Researchers
- Principal investigator: Kassab, Ghassan S — California Medical Innovations Institute
- Study coordinator: Kassab, Ghassan S
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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.