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

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2024 · $548,391

## Abstract

PROJECT SUMMARY: Disorganized electrical waves that produce complex arrhythmias in the heart are a
major health issue. According to the Center for Disease Control, an estimated 400,000-460,000 unexpected
sudden cardiac deaths occur every year in the US, in most cases due to ventricular fibrillation (VF). Atrial
fibrillation (AF) is the most common sustained cardiac arrhythmia; it contributes to 160,000 deaths
annually, and its prevalence is projected to increase from 5.2 million in 2010 to 12.1 million by 2030. The
primary defibrillation therapy for termination of VF and AF has significant side effects, including severe
pain and anxiety disorders from inappropriate firings along with myocardial damage from repeated shocks,
which can also lead to increased mortality. While there is no other reliable current cure for VF, AF can be
treated with radiofrequency ablation; however, its success rate for paroxysmal AF is only about 60%, and
for persistent AF it is even less effective at 30%. Thus, it is clear that there is a need for more effective
approaches to treat AF and VF.
 The ultimate goal of these studies is to develop a successful low-energy defibrillation protocol that
minimizes the energy requirements for suppression of VF and AF, to the point where such an approach
could be implemented reliably via an implantable device in ambulatory patients. Toward this end, the
project will utilize live explanted hearts from heart failure patients who receive new donor hearts, using
appropriate approvals. In this project, first the detailed dynamics of AF and VF in the explanted hearts will
be quantified, and advanced computational tools to simulate these arrhythmias in anatomically accurate
three-dimensional heart structures will be developed. Mathematical models of cardiac cells will be fitted to
experimental data to accurately reproduce the complex spatiotemporal dynamics of AF and VF in the
human heart. These models will be used in advanced computational tools to systematically study and
optimize the many parameters for low-energy defibrillation in simulations using machine learning
algorithms. Then, the defibrillation protocol will be tested and further optimized experimentally in the
explanted hearts and then with in vivo animal (swine) studies to investigate clinical translation. Throughout,
a state-of-the-art optical-mapping methodology will be used to visualize and record electrical activity to
characterize fibrillation and inform the mathematical models; similarly, the computational results will
inform low-energy defibrillation experiments in the explanted hearts. The use of explanted hearts not only
reduces the use of animals but sidesteps translational problems associated with studies in non-human
species, making the defibrillation studies proposed in this study as applicable to clinical settings as possible.
In the end, this project not only will lead to better cardioversion and defibrillation therapies with greater
reductions in pain, but als...

## Key facts

- **NIH application ID:** 10882677
- **Project number:** 2R01HL143450-05A1
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Flavio H Fenton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $548,391
- **Award type:** 2
- **Project period:** 2018-08-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10882677

## Citation

> US National Institutes of Health, RePORTER application 10882677, Integrative High-Resolution Experimental and Multiscale Modeling of Arrhythmias to Optimize Low Energy Anti-fibrillation Pacing (LEAP) (2R01HL143450-05A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10882677. Licensed CC0.

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