AI tool developed to identify risk of fatal heart condition

Researchers have developed an AI tool that can help doctors identify people at risk of developing a potentially fatal heart condition from an electrocardiogram (ECG). The tool, known as AIRE-CHB and supported by the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre, is due to go into clinical trials towards the end of 2025. It uses AI to detect the earliest signs of a serious condition called ‘complete heart block’, which can be fatal if left untreated. Heart block is caused by problems with electric signals moving from the upper to lower chambers of the heart as it beats. The signal can only get through sometimes or not at all, making the heart beat more slowly or skip beats. Doctors use clues from an ECG, which records the electrical activity of the heart and is one of the most common medical tests in the world. Dr Arunashis Sau, academic clinical lecturer at Imperial College London’s National Heart and Lung Institute, and cardiology registrar at Imperial College Healthcare NHS Trust, said: “When complete heart block occurs it can initially be intermittent and therefore difficult to identify; yet by the time it becomes permanent it is far more dangerous for the patient. “If we doctors can better identify patients early on with this condition, we’ll be able to monitor them more closely or progress to treating them with a pacemaker – avoiding their serious injury, emergency hospital admission, or even their death.” In a study, published in JAMA Cardiology, researchers at Imperial College London and Imperial College Healthcare NHS Trust show how they have trained their AI tool to read an ECG and identify patients who will have future heart block problems. Researchers at the trust analysed more than 1.1 million ECG heart recordings from nearly 190,000 patients at a Boston Hospital and used the data to train an AI system to recognise patterns that predict future complete heart block. The tool was tested on a separate group of over 50,000 people from the UK Biobank. AIRE-CHB performed better than existing methods for predicting heart block. It was able to correctly identify the risk of the patient developing complete heart block in 89% of cases, compared to the existing standard of 59% being identified correctly. The researchers believe the tool could be especially valuable for evaluating patients who have unexplained fainting episodes, which sometimes may be due to heart block. The cause of these episodes could be diagnosed earlier using AIRE-CHB. People identified as high-risk by AIRE-CHB were about seven to 12 times more likely to develop complete heart block compared to the individuals identified as low-risk. The research follows on from Imperial’s development of the related AI-ECG risk estimation model, known as AIRE, which can predict patients’ risk of developing or worsening disease from an ECG. Trials of AIRE in the NHS are planned for late 2025. These will evaluate the benefits of implementing the model with real patients from hospitals across Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust.