Incidence and predictors of thermal oesophageal and vagus nerve injuries in Ablation Index-guided high-power-short-duration ablation of atrial fibrillation: a prospective study

Abstract

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 aims to investigate incidence and predictors of thermal injuries, employing machine learning.

Methods and results: 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).

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.

Keywords: Atrial fibrillation; Catheter ablation; HPSD; Machine learning; Thermal injuries.