About this trial

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.

Eligibility criteria

Qualifiers

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

Disqualifiers

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

Trial design

Treatments tested in this trial

  • Machine learning

Treatment groups

10,000 Participants
are divided into 2 treatment groups

Locations

3
Guys' and St Thomas' Hospital NHS TrustSE1 7EH, London United Kingdom
Imperial College London Healthcare TrustW12 0HS, London United Kingdom
Kings' College London Healthcare TrustSE5 9RS, London United Kingdom

Sponsors and collaborators

Guy's and St Thomas' NHS Foundation Trust

Lead sponsor

Imperial College Healthcare NHS Trust

Collaborator

King's College Hospital NHS Trust

Collaborator