Celentano Eduardo, Cristiano Ernesto, Schena Stefano, Gasparri Mario, Ignatiuk Barbara, Renda Martina, Bia Elena, Rainone Raffaele, Graniero Ascanio, Giroletti Laura, Agnino Alfonso, De Groot Natasja M S
Department of Electrophysiology, Humanitas Gavazzeni, Bergamo, Italy.
Unit Translational Electrophysiology, Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Heart Rhythm O2. 2024 Dec 4;6(3):280-289. doi: 10.1016/j.hroo.2024.11.023. eCollection 2025 Mar.
Hybrid ablation can manage persistent atrial fibrillation (PsAF) and long-standing persistent atrial fibrillation (LSPAF). Robotic-enhanced hybrid ablation (RE-HA) offers greater precision and stability. However, biophysical predictors of effective local epicardial radiofrequency ablation (ELRF) during epicardial ablation are unknown.
The purpose of this study was to compare the time course of biophysical predictors of ELRF and no-ELRF during the first stage of RE-HA in patients with PsAF and LSPAF.
We conducted a dual-center retrospective cohort study involving 92 consecutive patients with PsAF or LSPAF who underwent RE-HA between January 2021 and May 2024. Epicardial electrogram disappearance, defined as a reduction of bipolar voltages to <0.05 mV, baseline impedance (BI), and impedance drop (ID), were compared between ELRF and no-ELRF cases. Univariate and multivariate logistic regression models were used to identify predictive variables. Optimal cutoff values were determined using receiver operating characteristic curves.
Among 2474 radiofrequency (RF) applications, significant predictors of ELRF included BI and ID at 1 and 8 seconds, with optimal cutoff values of <107, 0-7, and 5-17 Ω. The composite predictive model had an area under the receiver operating characteristic of 0.775, with 94% sensitivity, 53% specificity, and 65% accuracy. Our predictive ELRF score ranged from 0-4, and the Youden J test identifying a cutoff value of 3 as optimal.
BI and progressive ID were strong predictors of local epicardial RE-HA efficacy. The composite model was a reliable tool for early identification of ELRF, potentially reducing RF delivery and enhancing procedural efficiency. Larger prospective studies are needed to validate these findings.
混合消融可用于治疗持续性心房颤动（PsAF）和长期持续性心房颤动（LSPAF）。机器人辅助混合消融（RE-HA）具有更高的精确性和稳定性。然而，心外膜消融期间有效局部心外膜射频消融（ELRF）的生物物理预测指标尚不清楚。
本研究旨在比较PsAF和LSPAF患者在RE-HA第一阶段ELRF和非ELRF的生物物理预测指标的时间进程。
我们进行了一项双中心回顾性队列研究，纳入了2021年1月至2024年5月期间连续92例接受RE-HA的PsAF或LSPAF患者。比较了ELRF和非ELRF病例的心外膜电图消失情况（定义为双极电压降至<0.05 mV）、基线阻抗（BI）和阻抗下降（ID）。采用单因素和多因素逻辑回归模型确定预测变量。使用受试者工作特征曲线确定最佳截断值。
在2474次射频（RF）应用中，ELRF的显著预测指标包括1秒和8秒时的BI和ID，最佳截断值分别为<107、0-7和5-17Ω。复合预测模型的受试者工作特征曲线下面积为0.775，敏感性为94%，特异性为53%，准确性为65%。我们的ELRF预测评分范围为0-4，约登J检验确定截断值为3时最佳。
BI和进行性ID是局部心外膜RE-HA疗效的有力预测指标。复合模型是早期识别ELRF的可靠工具，可能减少RF传递并提高手术效率。需要更大规模的前瞻性研究来验证这些发现。
