# Molecular Mechanisms of Atrial Fibrillation: A Multimodal Analysis

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2024 · $500,891

## Abstract

Abstract
Atrial fibrillation (AF) is the most common arrhythmia especially in the aging population. It is associated with
increased risk of mortality and morbidity. At the present time, management of AF has focused on risk factor
modification, rate or rhythm control and anticoagulation. Evolution of clinical trials in the management of AF have
revealed that ablation seems superior in reducing the burden of AF and controlling the symptoms compared to
pharmaceutical agents. However, the benefit of ablation decreases over time and patients frequently require
more than one ablation. Earlier ablation in the course of the disease is more beneficial as failure of therapy is
related to duration of AF and size of left atrium. After two decades of investigations with varying methods of
ablation, we have only marginally improved the clinical outcome. The ablation procedure is time consuming and
only a fraction of patients are undergoing this procedure. A robust criterion of prediction of successful ablation
will be beneficial for patient selection and maximize the utilization of invasive therapies.
With this highly collaborative and multiscale study, our long-term goal is to develop effective models and discover
factors that indicate severity of AF that can be helpful as therapeutic targets and to predict prognosis. Our
objective is to identify patients who have increased risk of recurrence after ablation for AF by taking advantage
of the intracardiac electrograms from left atrial map and inflammatory biomarkers from blood samples obtained
pre-procedure. The central hypothesis is that domain-specific machine learning/ artificial intelligence algorithms
derived from multimodal data can predict the type of AF, severity of AF as indicated by abnormal areas in the
left atrium and clinical outcomes of AF ablation. To directly test the hypothesis, we will enroll prospective
consecutive consenting patients who present for AF ablation therapy. Pre-and post-ablation left atrial map and
blood samples drawn for biomarker analyses will be used for study purposes.

## Key facts

- **NIH application ID:** 11197308
- **Project number:** 7R01HL170520-02
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Nipavan Chiamvimonvat
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $500,891
- **Award type:** 7
- **Project period:** 2024-04-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11197308

## Citation

> US National Institutes of Health, RePORTER application 11197308, Molecular Mechanisms of Atrial Fibrillation: A Multimodal Analysis (7R01HL170520-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11197308. Licensed CC0.

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