Evidence map›Paper›PMID 41695517›Full record

ArticleHeart rhythm O22026

Transthoracic echocardiographic and artificial intelligence-enabled electrocardiography predictors of atrial arrhythmia recurrence after surgical ablation.

Dylan Goings, Ikram U Haq, Arman Arghami, Zachi Attia, Gabor Bagameri, Michael Brandt, Freddy Del-Carpio Munoz, Paul A Friedman, Kimberly A Holst, Peter A Noseworthy and 3 more

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Article in Heart rhythm O2, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

13 authors.

Dylan GoingsDepartment of Internal Medicine Mayo Clinic, Rochester, Minnesota.
Ikram U HaqDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Arman ArghamiDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Zachi AttiaDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Gabor BagameriDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Michael BrandtDepartment of Internal Medicine Mayo Clinic, Rochester, Minnesota.
Freddy Del-Carpio MunozDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Paul A FriedmanDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Kimberly A HolstDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Peter A NoseworthyDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Konstantinos C SiontisDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Alan SugrueDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Ammar M KilluDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recurrence of atrial fibrillation (AF)/flutter (AFl) after surgical ablation remains difficult to predict. Integration of novel biomarkers may enhance risk stratification. Objective: This study aimed to assess whether combining preoperative transthoracic echocardiography (TTE) and artificial intelligence-enabled electrocardiography (AI-ECG) scores improves the prediction of AF/AFl recurrence after surgical ablation. Methods: We retrospectively analyzed 1696 patients who underwent surgical AF/AFl ablation from 2006 to 2025 with available preoperative TTE and ECG and postblanking (90-day) ECG follow-up. Clinical, TTE, and AI-ECG variables (AF probability, ECG-estimated age, heart failure with preserved ejection fraction, left ventricular dysfunction, and aortic stenosis scores) were assessed. Cox proportional hazards and random survival forest models (80:20 train-test split) identified predictors of recurrence. Results: Among 1696 patients (mean age 67.3 ± 10.2 years; 61.7% male), 949 (56%) had AF/AFl recurrence over a median 3.14-year follow-up. Patients with recurrence had larger left atrial area (30.4 vs 24.5 cm Conclusion: Preoperative AI-ECG biomarkers (AF probability, age discordance) and TTE markers of atrial remodeling independently predicted AF/AFl recurrence after surgical AF/AFl ablation. Integration of these metrics improved risk stratification.

Indexed as

Arrhythmia recurrenceArtificial intelligenceAtrial fibrillationEchocardiographyElectrocardiographyLeft atrial remodelingMachine learningPrognostic modelingRisk predictionSurgical ablation

Identifiers

PMID41695517
PMCPMC12902217

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