About this trial
The investigator's project proposes the development of a 3D hybrid guidance system which has the aim of avoidance of scar and septal perforation through targeted lead deployment via a personalised septal real time image overlay onto x-ray fluoroscopy imaging during left bundle branch pacing. The investigators hypothesise that the use of cardiac anatomy and myocardial scar distribution derived from cardiac magnetic resonance imaging (MRI) as well as 3D position of the pacing lead, may improve LBBAP lead deployment success and improve clinical outcomes by guiding the physician towards optimal lead positioning.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
All patients who took part in the TACTIC-CRT (IRAS 250715) and Cardiac CT to guide Cardiac Resynchronisation Therapy (CRT) implantation (IRAS 150161) study.
All patients who received a LBBA pacing device from the GSTT/RBH/ICH database in the last 10 years (from 01/01/2014) and had a cardiac MRI for assessment of scar.
All patients who are >18 years old.
Male and Female
Disqualifiers
All patients <18 years old
Patients with congenital heart disease
Patients who did not have a cardiac MRI before receiving a LBBA pacing device
Trial population
Anonymised images from patients who took part in the TACTIC-CRT (IRAS 250715) and Cardiac CT to guide Cardiac Resynchronisation Therapy (CRT) implantation (IRAS 150161) or who received left bundle branch pacing and had a cardiac MRI
Trial design
Other
Retrospective
Treatments tested in this trial
Machine learning with artificial intelligence software program
Other interventionThis is a retrospective observational study using cardiac MRI, cardiac CT and x-ray fluoroscopy images to develop a 3D hybrid guidance system with the use of deep learning methods.
Treatment groups
Trial outcomes
Primary outcomes
Detection of the position of the left bundle branch area on x-ray fluoroscopy
The accuracy of left bundle branch area detection will be measured as intersection-over-union (IoU) with the use of a convolutional neural network
Sponsors and contacts
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