The PICM Risk Prediction Study - Application of AI to Pacing
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Machine learning.
- Who it may be relevant to
- Registry conditions: Heart Failure, Pacemaker-Induced Cardiomyopathy, Pacemaker Complication. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United Kingdom
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Predictive Risk Algorithm for Development of Right Ventricular Pacing Induced Cardiomyopathy - a Step Towards Personalized Pacemaker Lead Deployment
Overview
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
- Other Machine learning
Analysis of data with machine learning methods
Primary outcome measures
- Primary aim [Time frame: 2.5 years]
Secondary outcome measures (6)
- Secondary aim [Time frame: 2.5 years]
- Tertiary aim [Time frame: 2.5 years]
- Quarternary aim [Time frame: 2.5 years]
- Quinary aim [Time frame: 2.5 years]
- Senary aims [Time frame: 2.5 years]
- Septenary aim [Time frame: 2.5 years]
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
United Kingdom · 3 centers
- Guys' and St Thomas' Hospital NHS Trust — London
- Kings' College London Healthcare Trust — London
- Imperial College London Healthcare Trust — London
Identifiers
NCT: NCT06449079 · 333705