OPTimising a Screening Program to Detect Pacemaker-associated Heart Failure Using Artificial Intelligence

NCT07372651 | Enrolling By Invitation

• 1 other identifier: 2025-NCT11
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

Pacemakers are an effective treatment for slow heart rates which improve symptoms and save lives. However, for some people pacemakers can cause heart failure (HF) because of the unnatural way in which they stimulate heart beats. In several studies conducted in West Yorkshire we showed that \~1/3 of patients with pacemakers have undiagnosed HF. We also showed that where HF is discovered, treating it with safe and inexpensive medications reduces the chances of being admitted to hospital or dying. However, detecting HF requires an echocardiogram (a heart ultrasound scan) which takes \~45 minutes, requires a skilled technician, and costs £120; or, to put it another way \~£540,000 to assess the \~4,500 patients cared for at our hospital. A new approach is needed. We think that using new technologies can improve our ability to screen for HF in people with pacemakers. We will test two approaches. First, we will assess whether a hand-held echocardiogram can measure heart function using artificial intelligence (AI) as accurately as a standard echocardiogram done by a skilled technician. Second, we will assess whether a finger-prick blood test can detect the presence of abnormal function as accurately as a standard echocardiogram.

Conditions

Pacemaker; Heart Failure

Keywords

Heart failure; Artificial intelligence; Pacemaker

Outcome Measures

Primary Outcomes (1)

• The accuracy of artificial intelligence derived left ventricular ejection fraction (LVEF) compared to expert sonographer measured LVEF

  Determined by the individual equivalence coefficient of artificial intelligence (AI) derived left ventricular ejection fraction (LVEF) versus to sonographer measured LVEF. The accuracy of AI LVEF will be compared to sonographer measured LVEF for non-inferiority. The pre-determined non-inferiority margin is 0.25 for the upper bound of the 95% confidence interval for this comparison.

  Day 1

Secondary Outcomes (3)

• Accuracy of artificial intelligence derived left ventricular ejection fraction of <50%

  Day 1

• Prediction of left ventricular ejection fraction <50%

  Day 1

• The utility of artificial intelligence echocardiography

  Day 1

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

Patients with previously implanted pacemakers for bradycardia.

You may qualify if:

• Adult patients aged ≥18 years
• Patients with existing pacemakers who have a right ventricular pacing burden ≥20%.
• Ability to provide informed consent

You may not qualify if:

• Patients who are unwilling or unable to provide informed consent.
• Patients known to have heart failure
• Patients with any previous measurement of left ventricular ejection fraction \<50%.
• Patients with conduction system pacemakers.
• Patients with cardiac resynchronisation therapy pacemakers/defibrillators.
• Patients with implantable cardioverter defibrillators.
• Patients with leadless pacemakers.

Sponsors & Collaborators

• Leeds Hospitals Charity: collaborator
• University of Leeds: lead

Study Sites (1)

Cardiovascular Research Facility, Leeds General Infirmary

Leeds, LS1 3EX, United Kingdom

Location

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Lecturer in Cardiology

Study Record Dates

• First Submitted: September 29, 2025
• First Posted: January 28, 2026
• Study Start: October 7, 2025
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: January 28, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)