# Integrative Experimental and Multiscale High Resolution ModeIntegrative Experimental and Multiscale High Resolution Modling of Atrial Arrhythmias to Optimize Low Energy Anti-fibrillation Pacing (LEAP)

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $373,520

## Abstract

PROJECT SUMMARY: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia: it contributes
to 80,000 deaths annually and affects approximately 3.4 million Americans, with a projected increase to 10 million over the
next 30 to 40 years. The primary electrical therapy for termination of AF, DC cardioversion, has significant side effects
including electroporation and tissue damage, in addition to risks from sedation that can result in aspiration of stomach
contents, pneumonia, and other problems. Radiofrequency ablation has a success rate of only up to 60% for paroxysmal
AF, but less than 30% for persistent AF. Approaches to manage AF are not all successful and improvements are needed.
 We propose to further study, optimize and bring closer to the clinic our developed low-energy electrical therapy for
AF suppression, low-energy antifibrillation pacing (LEAP). This consists of a train of 5 electrical pulses delivered at or near
the dominant frequency of the arrhythmia from two field electrodes, rather than from a point source. We have shown that
LEAP has a success rate of more than 94% and uses less than 10% the energy of cardioversion. LEAP suppresses AF by
virtual electrodes created at heterogeneities within the tissue, which permits overdrive or underdrive pacing of AF. We
hypothesize that synchronization is the mechanism by which AF is terminated via LEAP and thus, can be applied to any
animal species and be optimized to be used in humans and eventually to be used as treatment requiring very small energies.
 Our ex-vivo optical mapping (OM) experiments and in-vivo studies in intact dogs have demonstrated that LEAP
extinguishes AF with energies as low as 0.05 J, more than ten times less than conventional cardioversion. Given these
encouraging results, we plan to adopt an integrative approach to optimizing this technology for possible clinical use. (1)
We will develop fast-state-of-the-art 3D physiological and structural accurate computer models of AF, validated using OM
voltage data from dogs, pigs and explanted human hearts (obtained from the heart transplant program at Emory Hospital)
to better understand and distinguish arrhythmias between species, structures and sizes. (2) We will iteratively perform ex-
vivo AF experiments in dog, pigs and human hearts and computers simulations and in-vivo AF experiments in dogs and pigs
to test our synchronization hypothesis and use it to optimize electrode configurations, pulse waveforms and pulse timing
for AF suppression using the lowest energies possible (below the pain threshold), Thereby paving the way for development
of implantable devices as another methods for managing AF in patients.
 The findings from this research will not only lead to new and improved cardioversion therapies with greater
reductions in pain, but also will fundamentally advance our mechanistic understanding of AF from the combined ex
vivo Langendorff perfused dog, pig and human optical mapping and basket catheter ex...

## Key facts

- **NIH application ID:** 10153868
- **Project number:** 5R01HL143450-04
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Flavio H Fenton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $373,520
- **Award type:** 5
- **Project period:** 2018-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10153868

## Citation

> US National Institutes of Health, RePORTER application 10153868, Integrative Experimental and Multiscale High Resolution ModeIntegrative Experimental and Multiscale High Resolution Modling of Atrial Arrhythmias to Optimize Low Energy Anti-fibrillation Pacing (LEAP) (5R01HL143450-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10153868. Licensed CC0.

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