The PICM Risk Prediction Study - Application of AI to Pacing

Overview

Development of pacing induced cardiomyopathy (PICM) is correlated to a high morbidity as signified by an increase in heart failure admissions and mortality. At present a lack of data leads to a failure to identify patients who are at risk of PICM and would benefit from pre-selection to physiological pacing. In the light of the foregoing, there is an urgent need for novel non-invasive detection techniques which would aid risk stratification, offer a better understanding of the prevalence and incidence of PICM in individuals with pacing devices and the contribution of additional risk factors.

Retrospective review of patient characteristics including 12 lead resting electrocardiograms and imaging data (CMR, CT, echo, CXR and fluoroscopy of pacing leads) of patients with right sided ventricular pacing lead due to symptomatic bradycardia, who developed pacing induced cardiomyopathy (or need for CRT upgrade) versus patients who did not using supervised machine learning methods. Development of personalised predictive pacing algorithm to improve right ventricular lead placement, such as conduction system pacing or pre-emptive implantation of an additional left ventricular lead to prevent left ventricular dilatation and pacemaker-induced cardiomyopathy (PICM) with heart failure (left ventricular ejection fraction \<50% by Simpson method), hospitalisation or death with the use of the retrospective patient data through machine learning.

Conditions