Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, People's Republic of China.
Clin Interv Aging. 2022 Sep 25;17:1405-1421. doi: 10.2147/CIA.S376091. eCollection 2022.
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, =0.018), left ventricular ejection fraction (OR 0.943, 95% CI 0.905-0.982, =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.
目的:本研究旨在开发和验证一种预测射频导管消融后心房颤动复发风险的风险列线图模型。
方法:采用回顾性观察性研究方法,收集 2018 年 1 月至 2021 年 6 月在我院首次接受射频消融的 485 例心房颤动患者的数据。所有患者被随机分为训练队列(70%;n=340)和验证队列(30%;n=145)。采用单因素和多因素逻辑回归分析确定独立的危险因素。使用 R 软件建立预测列线图模型。通过一致性统计量(C 统计量)、校准图和决策曲线分析(DCA)分别对列线图的区分度、校准度和临床效果进行开发和评估。
结果:该列线图由 4 个变量组成,包括左心房直径(OR 1.057,95%CI 1.010-1.107,=0.018)、左心室射血分数(OR 0.943,95%CI 0.905-0.982,=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)。该列线图在训练队列中的 C 统计量为 0.741(95%CI:0.689-0.794),在验证队列中的 C 统计量为 0.750(95%CI:0.670-0.831)。校准图显示,训练和验证队列的预测值与观察值之间具有良好的一致性。决策曲线分析和临床影响曲线表明,该预测列线图具有一定的临床实用性。
结论:该列线图模型具有良好的区分度和准确性,能够直观、个体化地筛选出高危人群,对射频消融后患者心房颤动复发具有一定的预测价值。
Am J Transl Res. 2024-11-15
Front Cardiovasc Med. 2023-8-11
Herzschrittmacherther Elektrophysiol. 2022-3
Ann Noninvasive Electrocardiol. 2022-3
J Cardiovasc Med (Hagerstown). 2021-12-1
Circulation. 2021-9-14
Nutr Metab Cardiovasc Dis. 2021-3-10