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.
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.
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)
Single group
AI-ECG
Diagnostic testThe AI-ECG will take a digital ECG recording and produce a predictive report for risk of cardiovascular disease and death for each patient.
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.
Time from referral to completion of the clinical pathway
Time from referral letter from the General Practitioner to the Rapid Access Chest Pain Clinic service, to the point at which the diagnosis and management plan has been established. Time-to-diagnosis will be assessed using survival analysis techniques (e.g. Kaplan-Meier curves, log-rank test, Cox proportional hazards model).
Composite outcome of hospital admission with acute coronary syndrome and cardiovascular mortality.
Outcomes will be followed up for all patients via the Whole Systems Integrated Care (WSIC) data, which will capture any new coded diagnoses and mortality. Cardiovascular events and mortality will be compared using binary logistic regression models.
Statin and antihypertensive medication use at one year.
All prescriptions for patients in the study will be followed up with the Whole Systems Integrated Care (WSIC) data, capturing prescriptions of statin and antihypertensive in both the AI-ECG and control groups. Rate of prescriptions will be compared between groups using chi-square tests or linear regression, depending on data type.
Symptom burden at one year, assessed using the Rose Angina questionnaire-based angina quantification app.
We plan to utilise a patient-facing phone application to administer a symptom questionnaire at one year. This will also be available in a paper format which can be accessed by patients on request. We will analyse these patient-reported outcomes using qualitative analysis tools in addition to descriptive statistics to report trends.
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Jamil Mayet
Lead sponsor
Imperial College London
Sponsor institution
Imperial College Healthcare NHS Trust
Collaborator
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