Predicting long‐term freedom from atrial fibrillation after catheter ablation by a machine learning algorithm: Validation of the CAAP‐AF score

Journal of Arrhythmia. 2020;36(2):297-303 DOI 10.1002/joa3.12303

 

Journal Homepage

Journal Title: Journal of Arrhythmia

ISSN: 1880-4276 (Print); 1883-2148 (Online)

Publisher: Wiley

Society/Institution: Japanese Heart Rhythm Society

LCC Subject Category: Medicine: Internal medicine: Specialties of internal medicine: Diseases of the circulatory (Cardiovascular) system

Country of publisher: Australia

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS


Koichi Furui (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Itsuro Morishima (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Yasuhiro Morita (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Yasunori Kanzaki (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Kensuke Takagi (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Ruka Yoshida (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Hiroaki Nagai (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Naoki Watanabe (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Naoki Yoshioka (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Ryota Yamauchi (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Hideyuki Tsuboi (Department of Cardiology Ogaki Municipal Hospital Ogaki Japan)

Toyoaki Murohara (Department of Cardiology Nagoya University Graduate School of Medicine Nagoya Japan)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 45 weeks

 

Abstract | Full Text

Abstract Background Preprocedural clinical predictors of the successful maintenance of sinus rhythm may contribute to optimal treatment strategies for atrial fibrillation (AF). The CAAP‐AF score, a novel simple tool scored as 0‐13 points (including six independent variables) has been proposed to predict long‐term freedom from AF after catheter ablation. To clarify its reproducibility, we examined the CAAP‐AF score's predictive performance and then created subgroups to best predict AF recurrence by using a machine learning algorithm. Methods We studied 583 consecutive patients who underwent initial AF catheter ablation at our institute (median CAAP‐AF score, 5; age, 66 ± 10 years old; female, 28.3%; coronary artery disease, 10.8%; left atrial diameter, 39.9 ± 6.6 mm; number of antiarrhythmic drugs failed, 0.4 ± 0.6; nonparoxysmal AF, 45.3%). All were systematically followed up with an endpoint of atrial tachyarrhythmia recurrence after the last ablation procedure. Results During the 1.8 ± 1.2‐year follow‐up, 157 patients had atrial tachyarrhythmia recurrence. Repeated procedures were performed (n = 115). Arrhythmia recurrence after the last session occurred in 69 patients. We created Kaplan‐Meier curves for freedom from AF after final AF ablation for ranges of CAAP‐AF scores; these confirmed the original study results. The machine learning using Classification and Regression Trees divided the patients into three categories by the risk score: low (score ≤5), intermediate (score 6‐8), and high (score ≥9). Conclusions The CAAP‐AF score was useful to stratify the atrial tachyarrhythmia recurrence risk in AF patients undergoing catheter ablation into three categories. The score should be considered when deciding whether to perform AF ablation in clinical practice.