# Heart Digital Twin Analysis of Arrhythmias due to Infiltrating Adiposity

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $740,783

## Abstract

PROJECT SUMMARY
Sudden cardiac death due to ventricular arrhythmias (VAs) is a leading cause of mortality world-wide. Despite
advancements in anti-arrhythmia therapies, VA rates remain high in part due to an incomplete understanding of
the underlying disease-induced arrhythmigenic substrate. In patients with prior myocardial infarction, traditional
dogma has maintained that heterogeneous scarring in the ventricles forms the arrhythmia substrate. While clinical
studies have utilized the visualization of ventricular scar in localizing VA ablation targets, these efforts have failed
to significantly improve VA recurrence rates, suggesting that scar characterization alone may be insufficient for
identifying and eliminating VA. Infiltrating adipose tissue (inFAT) is a newly recognized aspect of post-infarct
remodeling. However, because inFAT is intermingled with scar, the specific role of lnFAT In VA propensity In
patients with ischemic cardiomyopathy has never been explored.
The overall obJectlve of this appllcatlon Is to use a novel combination of mechanistic personallzed
computatlonal modellng ("dlgltal twin" of the heart), Imaging, electroanatomlcal mapping, ex-vivo human
heart experiments, and artificial intelligence (Al) to comprehensively characterize the role of inFAT
vs. scar in post-infarct VAs, and to develop a new digital-twin approach for guidance of VA ablation in
patients with ischemic cardiomyopathy. Leveraging our advancements in the acquisition of high-quality ventricular
images of scar and inFAT distribution, our expertise in personalized computational modeling and Al, and
our clinical and experimental expertise, we propose to develop personalized heart digital twins of ICM patinets
that incorporate scar and inFAT distributions and are parameterized with experimental data. Using the digital
twins and intra-procedure data, we will explore the mechanistic role of inFAT in arrhythmogenic propensity and
in the components of the VA circuit. We will also utilize the digital twinning technology to develop a comprehensive
VA ablation guidance strategy that accounts for the roles of inFAT and scar in VA circuits. The project
will culminate in a clinical translation feasibility study to demonstrate that the novel digital twins offer accurate
prediction of VA ablation sites, and can be used for pre-procedure guidance and optimal targeting, eliminating
extensive electroanatomical mapping. Successful execution of the proposed studies will provide new mechanistic
understanding of the role of inFAT in promoting and sustaining VAs, and will lead to significant improvements in
the clinical procedure of VA ablation. Completion of this project will also be a major leap forward in the integration
of imaging, computational modeling, intracardiac mapping, and Al in the treatment of heart rhythm disorders.

## Key facts

- **NIH application ID:** 10936167
- **Project number:** 1R01HL174440-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** NATALIA A. TRAYANOVA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $740,783
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10936167

## Citation

> US National Institutes of Health, RePORTER application 10936167, Heart Digital Twin Analysis of Arrhythmias due to Infiltrating Adiposity (1R01HL174440-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10936167. Licensed CC0.

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