Incidence and Predictors of Thermal Oesophageal and Vagus Nerve Injuries in Ablation Index Guided HPSD Ablation of Atrial Fibrillation: A Prospective Study.
Atrial fibrillation
HPSD
catheter ablation
machine learning
thermal injuries
Journal
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
ISSN: 1532-2092
Titre abrégé: Europace
Pays: England
ID NLM: 100883649
Informations de publication
Date de publication:
22 Apr 2024
22 Apr 2024
Historique:
received:
23
03
2024
accepted:
02
04
2024
medline:
22
4
2024
pubmed:
22
4
2024
entrez:
22
4
2024
Statut:
aheadofprint
Résumé
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. 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%; 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). 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.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 38646922
pii: 7655713
doi: 10.1093/europace/euae107
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT05709756']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.