Prospective Evaluation of AI-ECG for SHD Detection

Not yet recruiting

• 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)

• Registration Number: NCT07057466
• Lead Sponsor: Imperial College London

Brief Summary

This study aims to improve the early detection of undiagnosed heart disease, which causes serious health issues, hospital admissions, and high healthcare costs. Researchers are exploring how artificial intelligence (AI) can analyse routine heart tests, called electrocardiograms (ECGs), to detect heart problems. These tests can be done using both traditional ECG machines and portable, wearable devices like smartwatches, making it easier for people to monitor their heart health at home.

While AI has shown promise using past data, this study will involve the collection of ECG data and subsequent testing of its accuracy in real-world settings to ensure it works well for both doctors and patients. The goal is to see if AI can identify conditions like heart muscle weakness, valve issues, and high lung pressure from the ECG data of patients. The researchers will also compare AI's detections with other blood tests commonly used to diagnose heart disease.

The AI models that will be used are being tested for research and validation purposes only. They will not be used for clinical decision-making or providing information to influence diagnosis, treatment, or patient care during the study. The AI outputs are not shared with clinicians and will have no impact on the care pathway.

This research will demonstrate if AI-powered ECG analysis - whether from traditional or portable devices - can provide a low-cost, non-invasive way to detect heart disease early and improve health assessments.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2025-06-26
• Status Verified: 2025-07
• Study First Submitted QC: 2025-07-04
• Last Update Submitted: 2025-07-04
• Study First Posted: 2025-07-09
• Last Update Posted: 2025-07-09
• Study Start: 2025-08-04
• Primary Completion: 2027-05-03
• Study Completion: 2027-08-02

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 590

Inclusion Criteria

• Patients aged 18-90 years
• No prior formal diagnosis of HF (including systolic and diastolic dysfunction), PH, or VHD
• Ability to provide informed consent

Exclusion Criteria

• Severe arrhythmia or unstable cardiovascular disease
• Prior formal diagnosis of HF (including systolic and diastolic dysfunction), PH, or VHD
• Cardiac implantable electronic device in-situ, including a permanent pacemaker or implantable cardioverter defibrillator
• Involvement in current research or recent involvement in any research prior to recruitment

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
AI-ECG model classification performance for detection of structural heart disease (SHD)	From enrolment to end of patient's study visit (up to 1 hour)	AI-ECG model classification performance for HF, PH, and VHD, will be assessed for all ECG modalities (single-, 3-, 6-, and 12-lead ECGs) using the area under the receiver operating characteristic (AUROC; pre-defined threshold).

Secondary Outcome Measures

Name	Time	Method
Additional AI-ECG performance metrics for detection of SHD	From enrolment to end of patient's study visit (up to 1 hour)	Secondary performances measures will include sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and F1 score for all ECG modalities.
NT-proBNP performance metrics for detection of SHD	From enrolment to end of patient's study visit (up to 1 hour)	performances measures will include area under the receiver operating characteristic (AUROC; pre-defined threshold), sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and F1 score for all ECG modalities.
Combined AI-ECG and NT-proBNP performance analysis for detection of SHD	From enrolment to end of patient's study visit (up to 1 hour)	NT-proBNP and AI-ECG predictions will be combined in a logistic regression model to assess the performance of a combined approach.

Trial Locations

Locations (2)

Chelsea and Westminster Hospital; 🇬🇧 London, United Kingdom

West Middlesex University Hospital; 🇬🇧 London, United Kingdom