The PICM Risk Prediction Study - Application of AI to Pacing
- Conditions
- Pacemaker ComplicationHeart FailurePacemaker-Induced Cardiomyopathy
- Interventions
- Other: Machine learning
- Registration Number
- NCT06449079
- Lead Sponsor
- Guy's and St Thomas' NHS Foundation Trust
- 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.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 10000
- 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
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Pacing induced cardiomyopathy Machine learning Patients who received a pacing device and developed pacing induced cardiomyopathy Non-pacing induced cardiomyopathy Machine learning Patients who received a pacing device and did not develop pacing induced cardiomyopathy
- Primary Outcome Measures
Name Time Method Primary aim 2.5 years Number of risk factors in participants who developed pacing induced cardiomyopathy
- Secondary Outcome Measures
Name Time Method 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
Quinary aim 2.5 years 4. To establish the morbidity of PICM
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
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
Trial Locations
- Locations (3)
Guys' and St Thomas' Hospital NHS Trust
🇬🇧London, United Kingdom
Kings' College London Healthcare Trust
🇬🇧London, United Kingdom
Imperial College London Healthcare Trust
🇬🇧London, United Kingdom