Scholarlink

Objective <p>This study aimed to identify independent risk factors for atrial fibrillation recurrence after fluoroscopy-free radiofrequency ablation (RFA) in patients with paroxysmal atrial fibrillation (PAF), and to develop and validate a nomogram model for individualized prediction of postprocedural PAF recurrence risk.</p> Results <p>Clinical data of 204 PAF patients undergoing initial fluoroscopy-free RFA were retrospectively analyzed with a median 18.7-month follow-up, and 20.6% of patients had recurrence.Age, left atrial diameter, hypertension, and PAF disease duration were confirmed as independent recurrence risk factors. The validated nomogram showed excellent predictive performance, with C-indexes of 0.813 and 0.805 in the training and validation sets, respectively. Time-dependent ROC analyses revealed favorable AUC values for 6-month, 1-year and 2-year recurrence prediction. Calibration metrics and sensitivity analysis verified the model’s favorable stability and accuracy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A model for prediction of recurrence risk after zero-fluoroscopy catheter ablation for patients with paroxysmal atrial fibrillation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

查看全文
  • Lei Zhao,
  • Ruibin Li,
  • Long Bai,
  • Jidong Zhang

摘要

Objective

This study aimed to identify independent risk factors for atrial fibrillation recurrence after fluoroscopy-free radiofrequency ablation (RFA) in patients with paroxysmal atrial fibrillation (PAF), and to develop and validate a nomogram model for individualized prediction of postprocedural PAF recurrence risk.

Results

Clinical data of 204 PAF patients undergoing initial fluoroscopy-free RFA were retrospectively analyzed with a median 18.7-month follow-up, and 20.6% of patients had recurrence.Age, left atrial diameter, hypertension, and PAF disease duration were confirmed as independent recurrence risk factors. The validated nomogram showed excellent predictive performance, with C-indexes of 0.813 and 0.805 in the training and validation sets, respectively. Time-dependent ROC analyses revealed favorable AUC values for 6-month, 1-year and 2-year recurrence prediction. Calibration metrics and sensitivity analysis verified the model’s favorable stability and accuracy.