Predictive Risk Algorithm for Development of Right Ventricular Pacing Induced Cardiomyopathy - a Step Towards Personalized Pacemaker Lead Deployment
| Registry | NCT06449079 |
|---|---|
| Status | Not yet recruiting |
| Study type | Observational |
| Enrollment | 10000 (Estimated) |
| Sponsor | Guy's and St Thomas' NHS Foundation Trust (Other); collaborators: Imperial College Healthcare NHS Trust, King's College Hospital NHS Trust |
| Conditions | Heart Failure, Pacemaker-Induced Cardiomyopathy, Pacemaker Complication |
| Interventions | Machine learning (Other) |
| Dates | start 2024-07-30 (estimated), primary completion 2026-10-30 (estimated), completion 2026-10-30 (estimated), first posted 2024-06-07, last updated 2024-06-07 |
| Locations | 3 sites in United Kingdom |
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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.
| Arm | Type | Interventions | Description |
|---|---|---|---|
| Pacing induced cardiomyopathy | Other: Machine learning | Patients who received a pacing device and developed pacing induced cardiomyopathy | |
| Non-pacing induced cardiomyopathy | Other: Machine learning | Patients who received a pacing device and did not develop pacing induced cardiomyopathy |
Sex: All, ages: 18 Years to any
Inclusion Criteria: * All patients who received a pacing device (VVI, DDD, ICD, leadless pacemaker) from the GSTT/RBH/KCH/ICH database in the last 10 years (from 01/01/2014) * All patients who are >18 years old. * Male and Female Exclusion Criteria: * Patients who did not receive a pacing device (VVI, DDD, ICD, leadless pacemaker) * All patients <18 years old * Patients with congenital heart disease * Patients who have received artificial heart valves or underwent cardiac bypass surgery * Patients who did not have an echocardiogram after receiving a pacing device
| Facility | City | Country | Status |
|---|---|---|---|
| Guys' and St Thomas' Hospital NHS Trust | London | United Kingdom | |
| Kings' College London Healthcare Trust | London | United Kingdom | |
| Imperial College London Healthcare Trust | London | United Kingdom |