The PICM Risk Prediction Study - Application of AI to Pacing

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-07-30

Study Completion Date

2026-10-30

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Heart Failure Pacemaker-Induced Cardiomyopathy Pacemaker Complication

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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Pacing induced cardiomyopathy

Patients who received a pacing device and developed pacing induced cardiomyopathy

Machine learning

Intervention Type OTHER

Analysis of data with machine learning methods

Non-pacing induced cardiomyopathy

Patients who received a pacing device and did not develop pacing induced cardiomyopathy

Machine learning

Intervention Type OTHER

Analysis of data with machine learning methods

Interventions

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Machine learning

Analysis of data with machine learning methods

Intervention Type OTHER

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No