Artificial intelligence (AI)-enabled electrocardiography (ECG) can accurately predict an individual patient’s risk of future cardiovascular events as well as their short and long-term risk of dying, a study finds.
Existing AI-enabled ECG could predict disease and mortality but could not give sufficient information to guide clinical decisions for individual patients and were not adopted into practice, UK researchers wrote in The Lancet Digital Health.
To address the limitations of previous AI models, the team from Imperial College London and Imperial College Healthcare NHS Trust developed the AI-ECG risk estimator (AIRE) platform using data from a secondary care dataset of 1,163,401 ECGs taken from 189,539 patients.
Using a deep learning and a discrete-time survival model, researchers were able to create a patient-specific survival curve with a single ECG, allowing the AIRE platform to predict not only risk of mortality but also time-to-mortality.
The platform was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK, including volunteers, primary care patients, and secondary care patients.
Researchers found the model was able to identify the risk of death in the ten years following the ECG (from high to low) in 78% of cases, and was also able to predict future ventricular arrhythmia, future atherosclerotic cardiovascular disease, and future heart failure.
‘Through phenome-wide and genome-wide association studies, we also identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome,’ they wrote.
They concluded that clinicians could act on AIRE’s predictions to provide targeted, personalised and earlier intervention.
‘AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation,’ they said.
Lead author Dr Arunashis Sau, an academic clinical lecturer at Imperial College London’s National Heart and Lung Institute, and cardiology registrar at Imperial College Healthcare NHS Trust, said compared with cardiologists the AI model could detect more subtle detail in the ECGs.
‘It can ‘spot’ problems in ECGs that would appear normal to us, and potentially long before the disease develops fully,’ he said.
‘Our analysis shows that the AI can tell us a lot about not only about the heart but also what is going on elsewhere in the body and may be able to detect accelerated ageing.’
Dr Sau acknowledged it was necessary to see how the model performed in the healthcare system, but suggested it was possible that in the future, the technology could be used in a wearable device that provided doctors with continuous remote monitoring and a potential alert system.
Senior study author Dr Fu Siong Ng, Reader in Cardiac Electrophysiology at the National Heart & Lung Institute at Imperial College London, said the work had shown the AI model was a credible and reliable tool that could, in future, be programmed for use in different areas of the NHS to provide doctors with relevant risk information.
‘This could have a positive impact on how patients are treated, and ultimately improve patient longevity and quality of life, he said.
Dr Ng, who is also a consultant cardiologist at Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust, said the technology could also reduce waiting lists and allow more efficient allocation of resources.
Trials of the model are planned to start by mid-2025 in hospitals across Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust.
Participants are set to be recruited from outpatient clinics and from inpatient medical wards.
