Heart Failure, Pacemaker Complication, Pacemaker-Induced Cardiomyopathy
Conditions
Brief summary
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.
Detailed description
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.
Interventions
OTHERMachine learning
Analysis of data with machine learning methods
Sponsors
Imperial College Healthcare NHS Trust
King's College Hospital NHS Trust
Guy's and St Thomas' NHS Foundation Trust
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Primary aim | 2.5 years | Number of risk factors in participants who developed pacing induced cardiomyopathy |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Tertiary aim | 2.5 years | 2\. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the incidence of PCIM 2. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the incidence of PCIM |
| Quarternary aim | 2.5 years | 3.• To establish mortality of PICM |
| Secondary aim | 2.5 years | 1\. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the prevalence of pacemaker induced cardiomyopathy (PICM) |
| Senary aims | 2.5 years | 5.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of right ventricular lead position or leadless pacemaker position |
| Septenary aim | 2.5 years | 6.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of myocardial pathology from echocardiogram and cardiac MRI |
| Quinary aim | 2.5 years | 4\. To establish the morbidity of PICM |
Countries
United Kingdom
Outcome results
None listed