The PICM Risk Prediction Study - Application of AI to Pacing

Heart Failure Clinical Trial

Official title:

Predictive Risk Algorithm for Development of Right Ventricular Pacing Induced Cardiomyopathy - a Step Towards Personalized Pacemaker Lead Deployment

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06449079
• Other study ID #: 333705
• Status: Not yet recruiting
• Start date: July 30, 2024
• Est. completion date: October 30, 2026

Study information

• Verified date: June 2024
• Source: Guy's and St Thomas' NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 10000
• Est. completion date: October 30, 2026
• Est. primary completion date: October 30, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Cardiomyopathies
• Heart Failure
• Pacemaker Complication

Intervention

Other:
• Machine learning
Analysis of data with machine learning methods

Locations

Country	Name	City	State
United Kingdom	Guys' and St Thomas' Hospital NHS Trust	London
United Kingdom	Imperial College London Healthcare Trust	London
United Kingdom	Kings' College London Healthcare Trust	London

Sponsors (3)

Lead Sponsor	Collaborator
Guy's and St Thomas' NHS Foundation Trust	Imperial College Healthcare NHS Trust, King's College Hospital NHS Trust

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Primary aim	Number of risk factors in participants who developed pacing induced cardiomyopathy	2.5 years
Secondary	Secondary aim	1. To establish, through the GSTT/RBH/KCH/ICH RV-paced study population the prevalence of pacemaker induced cardiomyopathy (PICM)	2.5 years
Secondary	Tertiary aim	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	2.5 years
Secondary	Quarternary aim	3.• To establish mortality of PICM	2.5 years
Secondary	Quinary aim	4. To establish the morbidity of PICM	2.5 years
Secondary	Senary aims	5.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of right ventricular lead position or leadless pacemaker position	2.5 years
Secondary	Septenary aim	6.• To include predictive value for pacing induced cardiomyopathy risk with combination of imaging data of myocardial pathology from echocardiogram and cardiac MRI	2.5 years