AI-ECG Accessory Pathway Localisation Study (AAPLS)

Imperial College London

Status: Not yet enrolling

Conditions: ECG; Accessory Pathway; Artifical Intelligence

Study type: Observational

Funder types: Other

Identifiers

NCT07083791

IRAS 347921

Details and patient eligibility

About

This study seeks to validate the real-world accuracy of an AI-based algorithm for identifying the location of an accessory pathway from the 12-lead electrocardiogram

Full description

Silent validation study of an AI-ECG (artificial intelligence applied to electrocardiography) accessory pathway localisation algorithm, applied to prospective and consecutive cases in clinical practice, to determine its true accuracy and performance.

A pre-existing AI-ECG algorithm will be applied to participant ECG data, collected at the time of their clinical electrophysiology study (EPS) for ablation of their accessory pathway. This will be compared to the ground truth of the successful ablation location, determined by fluoroscopy and/or 3D electroanatomical mapping from their procedure.

Enrollment: 100 estimated patients

Sex: All

Ages: 13 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Referred for EPS procedure as part of their clinical care, with a finding of pre-excitation on their ECG
• Manifest pre-excitation on their ECG any time prior to their procedure
• Able to give consent
• Minimum age 13 years old
• Maximum age 100 years old

Exclusion criteria

• Unable to give consent
• Adults > 100 years old
• Children < 13 years old
• Patients with known location of their accessory pathway from a previous EP study

Trial design

100 participants in 1 patient group

Patients with manifest pre-excitation

Description:

Patients with a previous ECG demonstrating manifest pre-excitation, referred for an electrophysiology study as part of their clinical care