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Prospective Evaluation of AI-ECG for SHD Detection

Study acronym: AI-ECG-SHD
StatusRecruiting
PhaseNot specified
Started
View on ClinicalTrials.gov ↗
Trial flagged as At Risk
Primary endpoint reworded 1 of 1 changed
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Record History

  1. Primary endpoint(s) modified Study sites: 5 to 6 · Eligibility criteria expanded, about 56 words · Secondary endpoints: 3 to 4 Show 3 more changesShow less
    • Study sites: 5 to 6
    • Eligibility criteria expanded, about 56 words
    • Secondary endpoints: 3 to 4
  2. Study sites: 2 to 5v4
  3. Trial status: Not Yet Recruiting to Recruiting Start date changed from estimate of Aug 4, 2025 to Nov 4, 2025 · Site details revised at 2 of 2 sitesv3 Show 2 more changesShow less
    • Start date changed from estimate of Aug 4, 2025 to Nov 4, 2025
    • Site details revised at 2 of 2 sites
  4. Re-verified, no change to tracked fieldsv2
  5. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
Imperial College London
194 trials monitored
See 4 trials at risk
Heart Disease
2,136 trials monitored
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Heart Failure
1,871 trials monitored
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Hypertension
1,795 trials monitored
See 85 trials at risk
AI-ECG-SHD is a multicentre prospective observational diagnostic accuracy study evaluating artificial intelligence-enhanced electrocardiography (AI-ECG) for the detection of structural heart disease in adults undergoing clinically indicated transthoracic echocardiography (TTE). Participants undergo standard 12-lead ECG acquisition together with ECG recordings obtained using portable or wearable single-, 3-, and 6-lead devices. Established AI-ECG models will be evaluated for the detection of heart failure, pulmonary hypertension and valvular heart disease using TTE as the clinical reference standard, with performance compared across ECG acquisition modalities within the same prospective cohort. AI-ECG predictions are generated offline and are not provided to treating clinicians or used to alter clinical care. The study will also compare AI-ECG performance with N-terminal pro-B-type natriuretic peptide (NT-proBNP) and evaluate the diagnostic performance of a combined AI-ECG and NT-proBNP approach. The study aims to recruit 590 participants across six NHS hospital sites. A prespecified subgroup of 203 participants will undergo dedicated venous blood sampling for NT-proBNP; eligible same-day NT-proBNP measurements obtained as part of routine clinical care will also be included where available.
Trial Details
NCT Number NCT07057466
Lead Sponsor Imperial College London
Conditions Valvular Heart Disease Stenosis and Regurgitation (Diagnosis), Pulmonary Hypertension (Diagnosis), Heart Failure With Reduced Ejection Fraction (HFrEF; Diagnosis), Heart Failure With Preserved Ejection Fraction (HFpEF; Diagnosis)
Enrollment 590 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov