Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease.

ABSTRACT:

Background

Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation.

Methods

For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint.

Results

We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with a

SUBMITTER: Komlosi F 

PROVIDER: S-EPMC10740977 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature