Prospective Evaluation of AI-ECG for SHD Detection

Conditions studied

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)

About this study

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.

Interventions

• Other: Traditional 12-lead Electrocardiogram — 12-lead ECG investigation is a standard, non-invasive diagnostic procedure used as an intervention to assess participants' cardiac electrical activity. For the purposes of this study, 12-lead ECGs will be collected for the application of AI-ECG models for the detection of HF, VHD, and/or PH and will not be used to inform or alter patients' standard NHS care.
• Other: Apple Watch Series 4 Single-lead Electrocardiogram — Single-lead ECG taken using an Apple Watch Series 4 is a non-invasive, participant-initiated recording of cardiac electrical activity through a wearable device. While more limited than a 12-lead ECG, it can capture rhythm abnormalities-such as atrial fibrillation-and offers a convenient method for remote or continuous heart monitoring during the study. For the purposes of this study, single-lead ECGs will be collected for the application of AI-ECG models for the detection of HF, VHD, and/or PH and will not be used to inform or alter patients' standard NHS care.
• Other: Eko Core 500 Digital Stethoscope 3-lead Electrocardiogram — Three-lead ECG recorded using the Eko CORE 500 digital stethoscope is a non-invasive, clinician-operated cardiac assessment tool that captures real-time electrical activity of the heart during auscultation. It provides enhanced diagnostic information compared to single-lead recordings, allowing detection of arrhythmias and signs of structural heart disease at the point of care, supporting integrated clinical and digital assessment. For the purposes of this study, 3-lead ECGs will be collected for the application of AI-ECG models for the detection of HF, VHD, and/or PH and will not be used to inform or alter patients' standard NHS care.
• Other: AliveCor KardiaMobile Single- and 6-lead Electrocardiogram — A single- or 6-lead ECG recorded using the AliveCor KardiaMobile 6L device which is a portable, non-invasive method for capturing cardiac electrical activity. Operated by the participant or clinician, the device enables rapid rhythm assessment and detection of abnormalities such as atrial fibrillation. The 6-lead configuration offers more comprehensive data than single-lead recordings, supporting enhanced arrhythmia and conduction analysis in both in-clinic and remote settings. For the purposes of this study, single- and 6-lead ECGs will be collected for the application of AI-ECG models for the detection of HF, VHD, and/or PH and will not be used to inform or alter patients' standard NHS care.
• Other: Phlebotomy for N-terminal pro-B-type natriuretic peptide — A minimally invasive biomarker assessment used to evaluate cardiac wall stress and function. Elevated levels can indicate the presence or severity of heart failure and other forms of structural heart disease, making it a valuable tool for diagnosis, risk stratification, and monitoring of cardiac status throughout the study period. For the purposes of this study, NT-proBNP will be collected to assess its accuracy at detecting HF, PH, and VHD with comparison with AI-ECG detections. The investigators will also evaluate the accuracy of AI-ECG detections combined with NT-pro-BNP, for detecting HF, VHD, and PH. The investigators will not be using NT-proBNP results to inform or alter patients' standard NHS care.

Primary outcomes

• 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))

Eligibility information

Study locations

• Southmead Hospital, Bristol, United Kingdom
• Chelsea and Westminster Hospital, London, United Kingdom
• Hammersmith Hospital, London, United Kingdom
• St Mary's Hospital, London, United Kingdom
• West Middlesex University Hospital, London, United Kingdom