ArticleFrontiers in cardiovascular medicine2024
A novel nomogram for predicting the recurrence of atrial fibrillation in patients treated with first-time radiofrequency catheter ablation for atrial fibrillation.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrating traditional statistics and explainable machine learning for risk stratification of atrial fibrillation recurrence after pulsed field ablation: a hypothesis-generating single-center study.BMC cardiovascular disorders · 2026Article
- Development and external validation of a nomogram to predict the need for electrical cardioversion during radiofrequency ablation in patients with atrial fibrillation.International journal of cardiology. Heart & vasculature · 2025Article
- The Validation of a Nomogram for Predicting Recurrence in Patients with Non-Valvular Atrial Fibrillation Post-Ablation.Clinical interventions in aging · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: The purpose of this study was to investigate the predictive factors of atrial fibrillation (AF) recurrence in patients after first-time radiofrequency catheter ablation (RFCA) and to develop a nomogram predictive model that can provide valuable information for determining the ablation strategy. Methods: In total, 500 patients who had received first-time RFCA for AF were retrospectively enrolled in the study. The patients were divided into a training cohort ( Results: A total of 78 patients experienced the recurrence of AF after first-time RFCA in the training cohort. The six strongest predictors for AF recurrence in the training cohort were persistent AF, duration of AF, left atrial diameter (LAD), estimated glomerular filtration rate (eGFR), N-terminal pro-brain natriuretic peptide (NT-proBNP), and autoantibody against M2-muscarinic receptor (anti-M2-R). Based on the above six variables, a nomogram prediction model was constructed with a C-index of 0.862 (95% CI, 0.815-0.909), while the C-index was 0.831 (95% CI, 0.771-0.890) in the validation cohort. DCA showed that this nomogram had greater net benefits compared with other models. Furthermore, the nomogram showed a noticeable improvement in predictive performance, sensitivity, and reclassification for AF recurrence compared with the APPLE score, CAAP-AF score, or MB-LATER score. Conclusion: We established a novel predictive tool for AF recurrence after the first-time RFCA during a 2-year follow-up period that could accurately predict individual AF recurrence.
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