Cardiovascular scientists at City St George’s, University of London have identified crucial clues from a heart rhythm test that could detect hidden heart disease in young people, according to research published in the European Journal of Preventive Cardiology.
Published
The researchers say that these important findings could have implications for cardiac screening worldwide to help prevent sudden deaths.
Sudden cardiac death strikes without warning and is often the first sign of silent heart disease, with 12 young people dying of an undiagnosed heart condition every week in the UK. Diseases of the heart muscle called cardiomyopathies are among the most common causes of sudden cardiac death in people under the age of 35.
A 12-lead electrocardiogram (ECG) is a test that doctors use to monitor the electrical activity of the heart from 12 different angles across the chest, arms and legs. It can detect some signs of heart disease that cannot be seen otherwise.
A particular feature of the ECG, called the T wave, represents the relaxation of the heart’s main pumping chambers before the next heartbeat. In some people, this T wave will be abnormal, appearing upside down on the test, which is an early sign that a person might have a cardiomyopathy.
Delving deeper into ECGs
Researchers analysed ECG data from 5,360 people aged 14-35 who underwent voluntary cardiac screening by the charity Cardiac Risk in the Young (CRY). They investigated the depth and distribution patterns of the 12-lead ECG readings and looked at the association with cardiomyopathy diagnoses over an average of eight years.
An abnormal TWI was documented in 2% of cases (120 people). Of those, 13% (16 people) were diagnosed with cardiomyopathy. No deaths occurred during the follow-up period, but among those diagnosed with cardiomyopathy, three people had a sudden cardiac arrest, highlighting the serious nature of these conditions when left undetected.
People with deeper T waves on their ECG – measuring greater than 0.183 millivolts – were 18 times more likely to receive a cardiomyopathy diagnosis, compared with people who had a T wave inversion due to normal changes in the heart.
The team also found that T wave abnormalities affecting multiple areas of the heart, as opposed to just one region, were seven times more likely to indicate underlying disease.
A more systematic approach
Dr Gherardo Finocchiaro and the team now hope to validate these findings in a larger study, and explore the use of artificial intelligence and machine learning to analyse recordings at a much faster pace.