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

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

Interventions

DIAGNOSTIC_TEST

AI-ECG

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

Sponsors & Collaborators

• Imperial College Healthcare NHS Trust

  collaborator OTHER

• Jamil Mayet

  lead OTHER

Principal Investigators

• Jamil Mayet, MBChB · Imperial College NHS Healthcare Trust

Study Design

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-08-31

Primary Completion

2029-01-31

Completion

2029-01-31

Countries

• United Kingdom

Study Locations