Clinical trial NCT06449079: The PICM Risk Prediction Study - Application of AI to Pacing
Official title: Predictive Risk Algorithm for Development of Right Ventricular Pacing Induced Cardiomyopathy - a Step Towards Personalized Pacemaker Lead Deployment
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Heart Failure
Pacemaker-Induced Cardiomyopathy
Pacemaker Complication
Lead sponsor: Guy's and St Thomas' NHS Foundation Trust
Sponsor class: OTHER
Collaborators: name: Imperial College Healthcare NHS Trust
name: King's College Hospital NHS Trust
First posted: 2024-06-07
Enrollment: 10000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients who received a pacemaker at GSTT and RBH in the last 10 years and all patients who received a pacemaker at KCH and ICH in the last 3 years.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Primary aim
timeFrame: 2.5 years
Secondary outcomes:
measure: Secondary aim
timeFrame: 2.5 years
measure: Tertiary aim
timeFrame: 2.5 years
measure: Quarternary aim
timeFrame: 2.5 years
measure: Quinary aim
timeFrame: 2.5 years
measure: Senary aims
timeFrame: 2.5 years
measure: Septenary aim
timeFrame: 2.5 years
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Guys' and St Thomas' Hospital NHS Trust
city: London
country: United Kingdom
facility: Kings' College London Healthcare Trust
city: London
country: United Kingdom
facility: Imperial College London Healthcare Trust
Study officials:
References: