# 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 · $19,217

## Abstract

Project Summary for the supplement award
 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.
 In this supplement, we propose to further study and optimize 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.
 In this supplement we are extending our implementation of LEAP to be delivered by a rotating field instead of a
static field. This is a natural extension to our study, that grew out of discussions by the group team while performing and
analyzing LEAP experiments. This is a perfect extension project for a graduate student to take and complete in the remaining
time of the parent grant, as many of the required programs and experimental setups are already in place. The graduate
student (Giraldo Pino) will extend the numerical simulations to study the effect of LEAP delivered by a rotating electric
field, rather than a stationary one, he will also extend the LEAP experiments in porcine atria by implementing a rotating
field in the experimental setup and also perform experiments. The hypothesis for this project, is that defibrillation has been
shown to require lower energies when the field is applied along the axis of the cardiac fibers. Since the atrium has a complex
anatomy with fibers rotating in various degrees through the right and left atria, it is expected that a rotating field would be
able to excite intramural virtual electrodes with lower energies. This project adopts an integrative approach to optimize
LEAP suing simultaneously simulations and experiments in porcine atria, therefore this project will train Mr. Pino in
different areas and generate many new skills.
 Mr. Pino will iteratively...

## Key facts

- **NIH application ID:** 10250771
- **Project number:** 3R01HL143450-03S1
- **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:** $19,217
- **Award type:** 3
- **Project period:** 2018-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10250771

## Citation

> US National Institutes of Health, RePORTER application 10250771, Integrative Experimental and Multiscale High Resolution ModeIntegrative Experimental and Multiscale High Resolution Modling of Atrial Arrhythmias to Optimize Low Energy Anti-fibrillation Pacing (LEAP) (3R01HL143450-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10250771. Licensed CC0.

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