The Use of Artificial Intelligence-Enhanced Electrocardiograms in the Chest Pain Clinic to Risk Stratify Patients, Provide Rapid Reassurance and Enable a Low-Cost Clinical Pathway

NCT07757425 | Not Yet Recruiting

• 2 other identifiers: 26/LO/0288352261
• Study Type: interventional
• Target: 4,000
• Locations: 1 country
• Sites: 2

Brief Summary

Our current pathway for investigating patients with chest pain differs depending on if the pain is cardiac sounding or not. National guidelines advise us that patients with non-cardiac chest pain do not need further tests beyond seeing a clinician and having a test called an electrocardiogram (ECG), but often we do unnecessary additional investigations for these patients. Some of the tests we do involve invasive procedures or radiation, which have associated risks. We have recently developed an artificial intelligence (AI) ECG technology, which has been shown in various studies to reliably predict risk of heart disease, including heart attacks and death, from just one AI-ECG reading, which is a test that is painless with no radiation. We have shown that this AI-ECG is more accurate at predicting outcomes than the standard risk prediction models we use now. We propose investigating whether this new technology helps to nudge our clinicians to avoid risk averse behaviour so that they undertake fewer unnecessary investigations, by comparing its use to our current treatment pathway. The main questions our study aims to answer are:

• Will an AI-ECG assisted chest pain clinic pathway result in lower healthcare resource costs than the standard pathway?
• Will an AI-ECG assisted chest pain clinic pathway reduce the time from referral to diagnosis and treatment?
• Will an AI-ECG assisted chest pain clinic pathway perform equally as well as our current pathway in resolving symptoms and preventing future heart disease? We will randomly allocate half of the patients with non-cardiac pain in our chest pain clinics to have an AI-ECG, using it to determine which patients are low risk and which are higher risk. Feedback from the analysis will be given to the assessing clinician, with our hypothesis being that patients triaged as low risk by the AI-ECG will be reassured and discharged from clinic, with patients identified as higher risk undergoing further investigation. The other half of patients not allocated to receive an additional AI-ECG test will be managed as usual. All patients' clinical assessment and management plans will be assessed by a Consultant Cardiologist, who will not have access to the AI-ECG data so that there is assurance that all assigned management pathways are clinically safe and appropriate. We will compare the cost spent for each group at one year, as well as how quickly we can provide a diagnosis/management plan to patients, the number of cardiac events and the number of patients prescribed cholesterol and blood pressure lowering medications. We propose that this study will allow us to safely reassure more patients with chest pain more quickly.

Conditions

Angina (Stable); Chest Pain Atypical Syndrome

Keywords

Artificial intelligence; AI-ECG; Risk-stratification; Low-risk chest pain

Outcome Measures

Primary Outcomes (1)

• The healthcare utilisation costs of each pathway over a one-year period.

  The cost of each pathway will be assessed over a one-year period, taking an English NHS perspective. Healthcare resource use will include subsequent consultations with the general practitioner, outpatient cardiology appointments, accident and emergency attendances, inpatient admissions and additional investigations/interventions. This data will be extracted from the Whole Systems Integrated Care (WSIC) dashboard, which captures data on healthcare contacts across the North West London region. Data will be collected at 12 months following enrolment. Resource use will be valued using unit costs of health and social care from the Care and Outcomes Research Centre and national cost collection for the NHS. Differences in healthcare resource use and costs (both planned and unplanned) between the between the AI-ECG and the standard care pathways will be reported at 12 months.

  12 months following enrolment.

Secondary Outcomes (4)

• Time from referral to completion of the clinical pathway

  From enrolment to date of established diagnosis and management plan.

• Composite outcome of hospital admission with acute coronary syndrome and cardiovascular mortality.

  Measured up to one year from enrolment.

• Statin and antihypertensive medication use at one year.

  One year from enrolment.

• Symptom burden at one year, assessed using the Rose Angina questionnaire-based angina quantification app.

  One year following enrolment.

Study Arms (2)

AI-ECG arm

EXPERIMENTAL

Patients in this arm will have AI-ECG undertaken alongside their standard care. For patients with non-cardiac chest pain, the clinician will have access to the AI-ECG prediction result denoting the risk level from the ECG. This risk prediction will contribute to their clinical assessment of the patient in addition to history and examination.

Diagnostic Test: AI-ECG

Standard care

NO INTERVENTION

Patients will be reviewed in clinic as per best current practice. Decisions on their further care will be undertaken by clinicians as usual.

Interventions

AI-ECG DIAGNOSTIC_TEST

The AI-ECG will take a digital ECG recording and produce a predictive report for risk of cardiovascular disease and death for each patient.

AI-ECG arm

Eligibility Criteria

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

You may qualify if:

• Our study population will include all patients aged 18 years and older presenting with non-anginal chest pain (chest pain that is not typical to the heart) to 6 rapid access chest pain clinic sites across North West London. Chest pain typicality will be defined using the standardised Rose Angina questionnaire, based on clinical history.

You may not qualify if:

• Patients with typical cardiac (heart-related) chest pain, as defined by the Rose Angina classification
• Patients with known moderate or severe stenosis (narrowing) in an epicardial coronary artery (the blood vessels supplying the heart)
• Known left ventricular impairment (left ventricular ejection fraction \<50%), otherwise known as heart failure
• Left bundle branch block (a significant electrical abnormality of the heart on ECG)
• End-stage kidney failure requiring renal replacement therapy such as dialysis or a kidney transplant
• Moderate or severe valvular heart disease (serious narrowing or leaking of the heart valves)
• Paced rhythm at the time of ECG acquisition (lots of patients with pacemakers will fall into this category, but only if their pacemaker is firing at the time the ECG was taken).

Sponsors & Collaborators

• Jamil Mayet: lead
• Imperial College Healthcare NHS Trust: collaborator

Study Sites (2)

Imperial College NHS Healthcare Trust

London, London, United Kingdom

Location

London North West University Healthcare NHS Trust

London, United Kingdom

Location

Related Publications (1)

• Sau A, Pastika L, Sieliwonczyk E, Patlatzoglou K, Ribeiro AH, McGurk KA, Zeidaabadi B, Zhang H, Macierzanka K, Mandic D, Sabino E, Giatti L, Barreto SM, Camelo LDV, Tzoulaki I, O'Regan DP, Peters NS, Ware JS, Ribeiro ALP, Kramer DB, Waks JW, Ng FS. Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study. Lancet Digit Health. 2024 Nov;6(11):e791-e802. doi: 10.1016/S2589-7500(24)00172-9.

  PMID: 39455192 BACKGROUND

MeSH Terms

Conditions

Angina, Stable

Condition Hierarchy (Ancestors)

Angina Pectoris; Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Chest Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Jamil Mayet, MBChB

  Imperial College NHS Healthcare Trust

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jamil Mayet, Professor, MBChB

CONTACT

+44 020 7594 1006; j.mayet@imperial.ac.uk

Fu Siong Ng, Professor, MBBS

CONTACT

f.ng@imperial.ac.uk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: The reviewing Cardiologist will be masked as to the outcomes of any artificial-intelligence enhanced ECG analysis. The reviewing cardiologist will intervene if the managing clinician investigation plan is felt to be clinically inappropriate.
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor of Cardiology

Model Details: Cluster randomised

Study Record Dates

• First Submitted: August 6, 2026
• First Posted: August 11, 2026
• Study Start: August 1, 2026
• Primary Completion (Estimated): January 1, 2029
• Study Completion (Estimated): January 1, 2029
• Last Updated: August 11, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Start date: Immediately following publication of the primary results for a duration of 3 years.
• Access Criteria: Data will be shared with researchers whose proposed use is consistent with the informed consent provided by study participants. It will be made available upon reasonable request via email to the corresponding author.

Locations

GB (2)