Wolff Charlotte, Langenhan Katharina, Wolff Marc, Efimova Elena, Zachäus Markus, Darma Angeliki, Dinov Borislav, Seewöster Timm, Nedios Sotirios, Bertagnolli Livio, Wolff Jan, Paetsch Ingo, Jahnke Cosima, Bollmann Andreas, Hindricks Gerhard, Bode Kerstin, Halm Ulrich, Arya Arash
Department of Electrophysiology, Leipzig Heart Centre, Medical Faculty, Leipzig University, Strümpellstraße 39, 04289 Leipzig, Germany.
Department of Gastroenterology, Helios Park Clinic, Leipzig, Germany.
Europace. 2024 May 2;26(5). doi: 10.1093/europace/euae107.
High-power-short-duration (HPSD) ablation is an effective treatment for atrial fibrillation but poses risks of thermal injuries to the oesophagus and vagus nerve. This study aims to investigate incidence and predictors of thermal injuries, employing machine learning.
A prospective observational study was conducted at Leipzig Heart Centre, Germany, excluding patients with multiple prior ablations. All patients received Ablation Index-guided HPSD ablation and subsequent oesophagogastroduodenoscopy. A machine learning algorithm categorized ablation points by atrial location and analysed ablation data, including Ablation Index, focusing on the posterior wall. The study is registered in clinicaltrials.gov (NCT05709756). Between February 2021 and August 2023, 238 patients were enrolled, of whom 18 (7.6%; nine oesophagus, eight vagus nerve, one both) developed thermal injuries, including eight oesophageal erythemata, two ulcers, and no fistula. Higher mean force (15.8 ± 3.9 g vs. 13.6 ± 3.9 g, P = 0.022), ablation point quantity (61.50 ± 20.45 vs. 48.16 ± 19.60, P = 0.007), and total and maximum Ablation Index (24 114 ± 8765 vs. 18 894 ± 7863, P = 0.008; 499 ± 95 vs. 473 ± 44, P = 0.04, respectively) at the posterior wall, but not oesophagus location, correlated significantly with thermal injury occurrence. Patients with thermal injuries had significantly lower distances between left atrium and oesophagus (3.0 ± 1.5 mm vs. 4.4 ± 2.1 mm, P = 0.012) and smaller atrial surface areas (24.9 ± 6.5 cm2 vs. 29.5 ± 7.5 cm2, P = 0.032).
The low thermal lesion's rate (7.6%) during Ablation Index-guided HPSD ablation for atrial fibrillation is noteworthy. Machine learning based ablation data analysis identified several potential predictors of thermal injuries. The correlation between machine learning output and injury development suggests the potential for a clinical tool to enhance procedural safety.
高功率短持续时间(HPSD)消融是治疗心房颤动的有效方法,但存在食管和迷走神经热损伤风险。本研究旨在利用机器学习调查热损伤的发生率及预测因素。
在德国莱比锡心脏中心进行了一项前瞻性观察性研究,排除有多次既往消融史的患者。所有患者均接受消融指数引导下的HPSD消融及随后的食管胃十二指肠镜检查。一种机器学习算法按心房位置对消融点进行分类,并分析消融数据,包括消融指数,重点关注后壁。该研究已在clinicaltrials.gov注册(NCT05709756)。2021年2月至2023年8月期间,共纳入238例患者,其中18例(7.6%;9例食管、8例迷走神经、1例两者均有)发生热损伤,包括8例食管红斑、2例溃疡,无瘘管形成。后壁的平均力量更高(15.8±3.9 g对13.6±3.9 g,P = 0.022)、消融点数量更多(61.50±20.45对48.16±19.60,P = 0.007)以及总消融指数和最大消融指数更高(分别为24 114±8765对18 894±7863,P = 0.008;499±95对473±44,P = 0.04),但食管位置与热损伤发生无显著相关性。发生热损伤的患者左心房与食管之间的距离显著缩短(3.0±1.5 mm对4.4±2.1 mm,P = 0.012),心房表面积更小(24.9±6.5 cm²对29.5±7.5 cm²,P = 0.032)。
在消融指数引导下进行HPSD消融治疗心房颤动时,热损伤发生率较低(7.6%),这一点值得注意。基于机器学习的消融数据分析确定了热损伤的几个潜在预测因素。机器学习输出与损伤发生之间的相关性表明,有可能开发一种临床工具来提高手术安全性。