Askarinejad Amir, Sabahizadeh Amirreza, Kohansal Erfan, Ghasemi Zahra, Haghjoo Majid
Rajaie Cardiovascular Medical and Research Institute, Iran university of medical sciences, Tehran, Iran.
School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
BMC Cardiovasc Disord. 2024 Dec 19;24(1):711. doi: 10.1186/s12872-024-04367-z.
Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machine learning model to forecast the early recurrence of atrial fibrillation following catheter ablation.
A prospective longitudinal study was conducted using data from the Iranian AF registry. The dataset included 402 consecutive AF patients who underwent radiofrequency catheter ablation. The primary outcome was early recurrence of AF within 3 months' post-ablation. Data preprocessing and feature selection were performed, followed by the development and evaluation of various machine learning models. The CatBoost model was selected as the best-performing model.
The study population had a mean age of 57.30 ± 14.05 years, and 61.4% were male. AF recurrence occurred in 26.1% of patients. The CatBoost model, utilizing 35 features, achieved an accuracy of 92.5% in predicting AF recurrence. The model demonstrated high sensitivity (88.6%) and specificity (94.0%), with an area under the ROC curve of 0.96. Paroxysmal AF, BUN, Cr, age, mitral regurgitation, LA velocity, and mild valvular heart disease were among the most important predictive features.
Machine learning methods, particularly the CatBoost model, demonstrate high accuracy in predicting early catheter ablation outcomes in AF patients. The developed model has the potential to improve patient care and decision-making by identifying patients most likely to benefit from the procedure. Further studies with larger sample sizes and external validation are warranted to assess the generalizability of this method for catheter ablation outcome prediction in AF patients.
导管消融是心房颤动（AF）的一种常见治疗方法，但复发率仍存在差异。预测导管消融的成功率对于患者的选择和管理至关重要。本研究旨在创建一个机器学习模型，以预测导管消融后心房颤动的早期复发。
使用来自伊朗房颤登记处的数据进行了一项前瞻性纵向研究。该数据集包括402例连续接受射频导管消融的房颤患者。主要结局是消融后3个月内心房颤动的早期复发。进行了数据预处理和特征选择，随后开发并评估了各种机器学习模型。选择CatBoost模型作为表现最佳的模型。
研究人群的平均年龄为57.30±14.05岁，男性占61.4%。26.1%的患者发生了房颤复发。利用35个特征的CatBoost模型在预测房颤复发方面的准确率达到了92.5%。该模型表现出高敏感性（88.6%）和特异性（94.0%），ROC曲线下面积为0.96。阵发性房颤、血尿素氮、肌酐、年龄、二尖瓣反流、左房速度和轻度瓣膜性心脏病是最重要的预测特征。
机器学习方法，尤其是CatBoost模型，在预测房颤患者导管消融早期结局方面具有很高的准确性。所开发的模型有可能通过识别最有可能从该手术中获益的患者来改善患者护理和决策。有必要进行更大样本量的进一步研究和外部验证，以评估该方法在房颤患者导管消融结局预测中的通用性。
