The PICM Risk Prediction Study - Application of AI to Pacing

NCT06449079 | Not Yet Recruiting DMC

• 1 other identifier: 333705
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 3

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.

Conditions

Heart Failure; Pacemaker-Induced Cardiomyopathy; Pacemaker Complication

Outcome Measures

Primary Outcomes (1)

• Primary aim

  Number of risk factors in participants who developed pacing induced cardiomyopathy

  2.5 years

Secondary Outcomes (6)

• Secondary aim

  2.5 years

• Tertiary aim

  2.5 years

• Quarternary aim

  2.5 years

• Quinary aim

  2.5 years

• Senary aims

  2.5 years

Study Arms (2)

Pacing induced cardiomyopathy

Patients who received a pacing device and developed pacing induced cardiomyopathy

Other: Machine learning

Non-pacing induced cardiomyopathy

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

Other: Machine learning

Interventions

Machine learning OTHER

Analysis of data with machine learning methods

Non-pacing induced cardiomyopathy; Pacing induced cardiomyopathy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Guy's and St Thomas' NHS Foundation Trust: lead
• Imperial College Healthcare NHS Trust: collaborator
• King's College Hospital NHS Trust: collaborator

Study Sites (3)

Guys' and St Thomas' Hospital NHS Trust

London, SE1 7EH, United Kingdom

Location

Kings' College London Healthcare Trust

London, SE5 9RS, United Kingdom

Location

Imperial College London Healthcare Trust

London, W12 0HS, United Kingdom

Location

MeSH Terms

Conditions

Heart Failure

Interventions

Machine Learning

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Artificial Intelligence; Algorithms; Mathematical Concepts

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 25, 2024
• First Posted: June 7, 2024
• Study Start: July 30, 2024
• Primary Completion (Estimated): October 30, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: June 7, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share

Locations

GB (3)