Evidence map›Paper›PMID 41704263›Full record

ArticleFrontiers in cardiovascular medicine2026

A retrospective cohort and machine learning study on the efficacy, safety, and recurrence prediction of combined EI-VOM and linear ablation in persistent Non-valvular atrial fibrillation.

Ting Zhang, Yuling Dai, Wenzhai Cao

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Article in Frontiers in cardiovascular medicine, 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ting Zhang *Department of Health Humanities Research Center, Sichuan Vocational College of Health and Rehabilitation, Zigong, Sichuan, China.
Yuling Dai *Department of Cardiology, Jinniu District People's Hospital, Chengdu, Sichuan, China.
Wenzhai CaoDepartment of Cardiology, First People's Hospital of Zigong City, Zigong, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Although circumferential pulmonary vein isolation (CPVI) is the standard treatment for persistent nonvalvular atrial fibrillation (PeNVAF), its efficacy remains limited. Combining CPVI with linear ablation and ethanol infusion into the vein of Marshall (EI-VOM) (CPVI + PLUS) may improve patient outcomes. Method: This single-center retrospective study included hospitalized patients with PeNVAF who underwent their first radiofrequency ablation at Zigong First People's Hospital from August 2022 to August 2024. After propensity score matching (PSM), 132 patients were assigned to each group: the CPVI + PLUS group (combined EI-VOM and linear ablation) and the CPVI-only group. Outcomes were assessed at 3 and 6 months, including AF recurrence, complications, and cardiac function. Quality of life was evaluated at 6 months using the Atrial Fibrillation Effect on Quality-of-Life Questionnaire (AFEQT). The primary endpoints were AF recurrence rate and arrhythmia-free survival at 6 months after ablation; secondary endpoints included changes in cardiac function parameters, quality-of-life scores, and complication rates. Kaplan-Meier analysis was used to compare arrhythmia-free survival between the two groups, and univariate and multivariate logistic regression analyses were performed to identify risk factors for AF recurrence. In addition, a stacking ensemble model was developed to enhance prediction of AF recurrence after ablation. Results: There were no significant differences in baseline demographics, comorbidities, laboratory, or echocardiographic parameters between the two groups. The CPVI + PLUS group had a longer procedure time than the CPVI group, but intraoperative complication rates were similar. At 6-month follow-up, the CPVI + PLUS group showed significantly better AFEQT, left ventricular ejection fraction (LVEF), left atrial diameter(LAD), and left ventricular end-diastolic diameter (LVEDD) compared to the CPVI group. Recurrence rates of AF were lower in the CPVI + PLUS group, and arrhythmia-free survival time was longer. Multivariate analysis identified coronary artery disease, heart failure, severe preoperative valvular regurgitation, intraoperative myocardial injury, longer AF course, and larger LAD as independent risk factors for recurrence. Additional ablation strategies and left atrial appendage occlusion were protective factors. The stacking ensemble model achieved high accuracy, precision, recall, F1 score, and ROC-AUC in both training and validation cohorts. Feature importance analysis consistently identified AF course, age, body mass index, B-type natriuretic peptide, LVEF, platelet count, and uric acid as core predictors of recurrence risk. Conclusion: CPVI + PLUS significantly improved arrhythmia-free survival, cardiac function, and quality of life in patients with PeNVAF without increasing procedural risk. Multivariate and machine learning analyses confirmed that additional ablation and left atrial appendage occlusion were protective against recurrence, while heart failure, valvular regurgitation, and longer AF course increased risk. The stacking model showed excellent predictive performance, and key features identified may support precise risk stratification and individualized therapy.

Indexed as

atrial fibrillationcatheter ablationmachine learningrisk predictionvein of Marshall

Identifiers

PMID41704263
PMCPMC12908034

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.