Construction and validation of a regularized logistic regression model for predicting late recurrence after ablation in persistent atrial fibrillation: a clinical parameter approach

The regularized logistic regression model incorporating clinical parameters accurately predicts long-term recurrence risk post-ablation in PsAF patients, outperforming traditional risk scoring systems and showing significant potential for clinical application.

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Publication Details

Journal
Frontiers in Cardiovascular Medicine
Published
2025-12-12
DOI
https://doi.org/10.3389/fcvm.2025.1713560
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

Construction and validation of a regularized logistic regression model for predicting late recurrence after ablation in persistent atrial fibrillation: a clinical parameter approach

Ning Liang, Weijia Cui, Xiao-Hong Bin, Jinzhou Xie
Frontiers in Cardiovascular Medicine
Atrial Fibrillation Management and Outcomes
article

Construction and validation of a regularized logistic regression model for predicting late recurrence after ablation in persistent atrial fibrillation: a clinical parameter approach

Ning Liang, Weijia Cui, Xiao-Hong Bin, Jinzhou Xie
article en

Abstract

The regularized logistic regression model incorporating clinical parameters accurately predicts long-term recurrence risk post-ablation in PsAF patients, outperforming traditional risk scoring systems and showing significant potential for clinical application.

Frontiers in Cardiovascular MedicineVol. 12
Sichuan University (CN), West China Hospital of Sichuan University (CN), Chengdu Second People's Hospital (CN), Sichuan Provincial Hospital of Traditional Chinese Medicine (CN)
Openalex Percentile: Top 43%
Atrial Fibrillation Management and Outcomes
DOIOpen Access PDFOpenAlex
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