Incidence and Predictors of Thermal Oesophageal and Vagus Nerve Injuries in Ablation Index Guided HPSD Ablation of Atrial Fibrillation: A Prospective Study

Charlotte Wolff, Katharina Langenhan, Marc Wolff, Elena Efimova, Markus Zachäus, Angeliki Darma, Borislav Dinov, Timm Seewöster, Sotirios Nedios, Livio Bertagnolli, Jan Wolff, Ingo Paetsch, Cosima Jahnke, Andreas Bollmann, Gerhard Hindricks, Kerstin Bode, Ulrich Halm, Arash Arya
{"title":"Incidence and Predictors of Thermal Oesophageal and Vagus Nerve Injuries in Ablation Index Guided HPSD Ablation of Atrial Fibrillation: A Prospective Study","authors":"Charlotte Wolff, Katharina Langenhan, Marc Wolff, Elena Efimova, Markus Zachäus, Angeliki Darma, Borislav Dinov, Timm Seewöster, Sotirios Nedios, Livio Bertagnolli, Jan Wolff, Ingo Paetsch, Cosima Jahnke, Andreas Bollmann, Gerhard Hindricks, Kerstin Bode, Ulrich Halm, Arash Arya","doi":"10.1093/europace/euae107","DOIUrl":null,"url":null,"abstract":"Background and Aims 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 investigates incidence and predictors of thermal injuries, employing machine learning. Methods 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). Results Between February 2021, and August 2023, 238 patients were enrolled, of whom 18 (7.6%; 9 oesophagus, 8 vagus nerve, 1 both) developed thermal injuries, including 8 oesophageal erythemata, two ulcers and no fistula. Higher mean force (15.8±3.9g vs. 13.6±3.9g, p=0.022), ablation point quantity (61.50±20.45 vs. 48.16±19.60, p=0.007), total and maximum Ablation Index (24114±8765 vs. 18894±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±1.5mm vs 4.4±2.1mm, p=0.012) and smaller atrial surface areas (24.9±6.5 cm2 vs. 29.5±7.5cm2, p=0.032). Conclusion 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.","PeriodicalId":11720,"journal":{"name":"EP Europace","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EP Europace","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/europace/euae107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background and Aims 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 investigates incidence and predictors of thermal injuries, employing machine learning. Methods 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). Results Between February 2021, and August 2023, 238 patients were enrolled, of whom 18 (7.6%; 9 oesophagus, 8 vagus nerve, 1 both) developed thermal injuries, including 8 oesophageal erythemata, two ulcers and no fistula. Higher mean force (15.8±3.9g vs. 13.6±3.9g, p=0.022), ablation point quantity (61.50±20.45 vs. 48.16±19.60, p=0.007), total and maximum Ablation Index (24114±8765 vs. 18894±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±1.5mm vs 4.4±2.1mm, p=0.012) and smaller atrial surface areas (24.9±6.5 cm2 vs. 29.5±7.5cm2, p=0.032). Conclusion 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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
消融指数引导的心房颤动 HPSD 消融术中食管和迷走神经热损伤的发生率和预测因素:前瞻性研究
背景和目的 高功率短时程(HPSD)消融术是治疗心房颤动的有效方法,但存在对食道和迷走神经造成热损伤的风险。本研究利用机器学习技术调查了热损伤的发生率和预测因素。方法 德国莱比锡心脏中心进行了一项前瞻性观察研究,排除了之前进行过多次消融术的患者。所有患者都接受了消融指数引导的 HPSD 消融术和随后的食管胃十二指肠镜检查。机器学习算法根据心房位置对消融点进行分类,并分析包括消融指数在内的消融数据,重点关注后壁。该研究已在 clinicaltrials.gov (NCT05709756) 上注册。结果 2021 年 2 月至 2023 年 8 月期间,238 名患者入组,其中 18 人(7.6%;9 人食道,8 人迷走神经,1 人两者均有)出现热损伤,包括 8 例食道红斑、2 例溃疡,无瘘管。后壁较高的平均作用力(15.8±3.9g vs. 13.6±3.9g,p=0.022)、消融点数量(61.50±20.45 vs. 48.16±19.60,p=0.007)、总消融指数和最大消融指数(分别为24114±8765 vs. 18894±7863,p=0.008;499±95 vs. 473±44,p=0.04)与热损伤的发生有显著相关性,食管位置则无相关性。热损伤患者左心房与食管之间的距离明显较小(3.0±1.5mm vs 4.4±2.1mm,P=0.012),心房表面积较小(24.9±6.5cm2 vs 29.5±7.5cm2,P=0.032)。结论 在消融指数引导下进行心房颤动 HPSD 消融术的热损伤率较低(7.6%),值得注意。基于机器学习的消融数据分析确定了几种潜在的热损伤预测因子。机器学习输出与损伤发展之间的相关性表明,这种临床工具具有提高手术安全性的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
High lead-related complication rate with MicroPort Vega active fixation pacing leads. Dual chamber versus single chamber pacemaker in patients in sinus rhythm with an atrioventricular block: a nationwide cohort study Ventricular Arrhythmias in Acute Heart Failure. A Clinical Consensus Statement of the Association for Acute CardioVascular Care Association (ACVC), the European Heart Rhythm Association (EHRA) and the Heart Failure Association (HFA) of the ESC Enhancing Origin Prediction: Deep Learning Model for Diagnosing Premature Ventricular Contractions with Dual-Rhythm Analysis Focused on Cardiac Rotation A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1