Predictors of paroxysmal atrial fibrillation in patients with a cryptogenic stroke: Selecting patients for long-term rhythm monitoring

Samuel J Apple; Matthew Parker; David Flomenbaum; Shalom M Rosenbaum; Joshua Borck; Adrian Choppa; Pawel Borkowski; Vikyath Satish; Majd Al Deen Alhuarrat; John D Fisher; Luigi Di Biase; Andrew Krumerman; Kevin J Ferrick

doi:10.1016/j.hrthm.2024.07.004

Predictors of paroxysmal atrial fibrillation in patients with a cryptogenic stroke: Selecting patients for long-term rhythm monitoring

Heart Rhythm. 2024 Jul 4:S1547-5271(24)02875-3. doi: 10.1016/j.hrthm.2024.07.004. Online ahead of print.

Affiliations

¹ New York City Health and Hospitals/Jacobi Medical Center, Albert Einstein College of Medicine, Bronx, New York. Electronic address: SApple93@gmail.com.
² New York City Health and Hospitals/Jacobi Medical Center, Albert Einstein College of Medicine, Bronx, New York.
³ Montefiore Medical Center, Bronx, New York.
⁴ Northwell Health, Northern Westchester Hospital, Mount Kisco, New York.

PMID: 38969049
DOI: 10.1016/j.hrthm.2024.07.004

Abstract

Background: After a cryptogenic stroke, patients often will require prolonged cardiac monitoring; however, the subset of patients who would benefit from long-term rhythm monitoring is not clearly defined.

Objective: The purpose of this study was to create a risk score by identifying significant predictors of atrial fibrillation (AF) using age, sex, comorbidities, baseline 12-lead electrocardiogram, short-term rhythm monitoring, and echocardiographic data and to compare it to previously published risk scores.

Methods: Patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or transient ischemic attack who underwent long-term rhythm monitoring with an implantable cardiac monitor were retrospectively analyzed.

Results: Variables positively associated with a diagnosis of clinically significant AF include age (P <.001), race (P = .022), diabetes status (P = .026), chronic obstructive pulmonary disease status (P = .012), presence of atrial runs (P = .003), number of atrial runs per 24 hours (P <.001), total number of atrial run beats per 24 hours (P <.001), number of beats in the longest atrial run (P <.001), left atrial enlargement (P = .007), and at least mild mitral regurgitation (P = .009). We created a risk stratification score for our population, termed the ACL score. The ACL score demonstrated superiority to the CHA₂DS₂-VASc score and comparability to the C₂HEST score for predicting device-detected AF.

Conclusion: The ACL score enables clinicians to better predict which patients are more likely to be diagnosed with device-detected AF after a cryptogenic stroke.

Keywords: Atrial cardiomyopathy; Atrial fibrillation; Cryptogenic stroke; Implantable cardiac monitor; Long-term rhythm monitoring; Risk stratification.