# Machine Learning for Atrial Fibrillation Ablation

> **NIH NIH R21** · EMORY UNIVERSITY · 2021 · $112,499

## Abstract

SUMMARY
Affecting over 6 million people in the U.S., atrial fibrillation (AF), the most common cardiac arrhythmia, is a major
public health concern. AF is costly to the health care system and leads to significant health consequences (e.g.,
stroke, heart failure, dementia, decreased quality of life). With time, AF patients experience increased frequency
and duration of AF episodes. Random occurrence of sporadic AF episodes and the need for anticoagulation to
prevent stroke make AF difficult to manage. Many AF patients seek out atrial fibrillation ablation (AFA) in order
to improve quality of life and decrease AF episodes. AFA, cauterization of areas of the left atrium, is the most
effective treatment for persistent / paroxysmal AF. AFA success rates vary, but many patients will not be AF-free
following AFA. At leading AFA centers, AF-free rates at one and two years after initial AFA were 40% and 37%,
respectively. Given the modest success rates of AFA, patient selection for this procedure should receive more
attention. Sociodemographic and clinical phenotype data have been used to predict AFA response, but
collectively they have poor predictive ability. The widespread adoption of electronic health record (EHR) systems
presents a ripe opportunity for a paradigm shift for predicting AFA outcomes. A better understanding of patient
specific factors predicting AFA outcome will inform patient selection for this procedure. To this end we propose
to use machine learning techniques to develop predictive models for outcomes of primary AFA procedures,
addressing the following specific aims and research questions:
1. Aim 1: Predict adverse AFA outcomes using machine learning.
 • How well do existing risk scores predict AFA complications prior to initial procedure?
 • Can a machine learning model trained on EHR data provide better prediction of AFA complications?
2. Aim 2: Data-driven AFA outcome subgroup identification.
 • Can cluster analysis identify useful subgroups based on outcome trajectory?
 • Are other unsupervised ML algorithms such as sequential pattern mining alternatives for analyzing
 patient outcome trajectories?
3. Aim 3: Develop an open-source software toolkit.
This project will lay the foundation for future refinement of existing machine learning methods as well as
development of new methods to improve prediction of AF recurrence following AFA.

## Key facts

- **NIH application ID:** 10115455
- **Project number:** 1R21HL156184-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** VICKI Stover HERTZBERG
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $112,499
- **Award type:** 1
- **Project period:** 2021-09-08 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115455

## Citation

> US National Institutes of Health, RePORTER application 10115455, Machine Learning for Atrial Fibrillation Ablation (1R21HL156184-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10115455. Licensed CC0.

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