Purpose: This study aimed to develop and validate a risk nomogram model for predicting the risk of atrial fibrillation recurrence after radiofrequency catheter ablation.
Patients and Methods: A retrospective observational study was conducted using data from 485 patients with atrial fibrillation who underwent the first radiofrequency ablation in our hospital from January 2018 to June 2021. All patients were randomized into training cohort (70%; n=340) and validation cohort (30%; n=145). Univariate and multivariate logistic regression analyses were used to identify independent risk factors. The predictive nomogram model was established by using R software. The nomogram was developed and evaluated based on differentiation, calibration, and clinical efficacy by concordance statistic (C-statistic), calibration plots, and decision curve analysis (DCA), respectively.
Results: The nomogram was established by four variables including left atrial diameter (OR 1.057, 95% CI 1.010– 1.107, P=0.018), left ventricular ejection fraction (OR 0.943, 95% CI 0.905– 0.982, P=0.005), type of atrial fibrillation (OR 2.164, 95% CI: 1.262– 3.714), and systemic inflammation score (OR 1.905, 95% CI 1.408– 2.577). The C-statistic of the nomogram was 0.741 (95% CI: 0.689– 0.794) in the training cohort and 0.750 (95% CI: 0.670– 0.831) in the validation cohort. The calibration plots showed good agreement between the predictions and observations in the training and validation cohorts. Decision curve analysis and clinical impact curves indicated the clinical utility of the predictive nomogram.
Conclusion: The nomogram model has good discrimination and accuracy, which can screen high-risk groups intuitively and individually, and has a certain predictive value for atrial fibrillation recurrence in patients after radiofrequency ablation.

Keywords: nomogram, risk prediction model, atrial fibrillation, radiofrequency catheter ablation, recurrence


中文翻译：

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用于预测房颤患者射频导管消融术后复发的风险列线图模型的开发和验证

目的：本研究旨在开发和验证用于预测射频导管消融术后房颤复发风险的风险列线图模型。
患者和方法：使用来自 2018 年 1 月至 2021 年 6 月在我院接受首次射频消融的 485 例房颤患者的数据进行了一项回顾性观察研究。所有患者被随机分为训练队列（70%；n=340）和验证队列（30 %；n=145)。单变量和多变量逻辑回归分析用于识别独立危险因素。使用R软件建立预测列线图模型。列线图的开发和评估分别基于一致性统计（C-统计）、校准图和决策曲线分析（DCA）的分化、校准和临床疗效。
结果：列线图由四个变量建立，包括左心房直径 (OR 1.057, 95% CI 1.010–1.107, P=0.018)、左心室射血分数 (OR 0.943, 95% CI 0.905–0.982, P = 0.005)、心房颤动类型 (OR 2.164, 95% CI: 1.262–3.714) 和全身炎症评分 (OR 1.905, 95 % CI 1.408–2.577)。列线图的 C 统计量在训练队列中为 0.741（95% CI：0.689–0.794），在验证队列中为 0.750（95% CI：0.670–0.831）。校准图显示了训练和验证队列中的预测和观察结果之间的良好一致性。决策曲线分析和临床影响曲线表明了预测列线图的临床效用。
结论：列线图模型具有良好的区分性和准确性，可以直观、个体化地筛选高危人群，对射频消融术后患者的房颤复发具有一定的预测价值。

关键词：列线图，风险预测模型，心房颤动，射频导管消融，复发