London: Five subtypes of heart failure that could potentially be used to predict future risk for individual patients have been identified using artificial intelligence (AI) tools, according to a new study, led by an Indian-origin researcher.
Heart failure is an umbrella term for when the heart is unable to pump blood around the body properly. Current ways of classifying heart failure do not accurately predict how the disease is likely to progress.
For the study, published in Lancet Digital Health, researchers from the University College London looked at detailed anonymised patient data from more than 300,000 people aged 30 years or older who were diagnosed with heart failure in the UK over a span of 20 years.
Using several machine learning methods, they identified five subtypes: early onset, late onset, atrial fibrillation related (atrial fibrillation is a condition causing an irregular heart rhythm), metabolic (linked to obesity but with a low rate of cardiovascular disease), and cardiometabolic (linked to obesity and cardiovascular disease).
The researchers found differences between the subtypes in patients’ risk of dying in the year after diagnosis.
The all-cause mortality risks at one year were: early onset (20 per cent), late onset (46 per cent), atrial fibrillation related (61 per cent), metabolic (11 per cent), and cardiometabolic (37 per cent).
The team also developed an app that clinicians could potentially use to determine which subtype a person with heart failure has, which may potentially improve predictions of future risk and inform discussions with patients.
“We sought to improve how we classify heart failure, with the aim of better understanding the likely course of disease and communicating this to patients. Currently, how the disease progresses is hard to predict for individual patients. Some people will be stable for many years, while others get worse quickly,” said lead author Professor Amitava Banerjee from UCL’s Institute of Health Informatics.
“Better distinctions between types of heart failure may also lead to more targeted treatments and may help us to think in a different way about potential therapies. In this new study, we identified five robust subtypes using multiple machine learning methods and multiple datasets,” he added.
The next step, Banerjee said, is to see if this way of classifying heart failure can make a practical difference to patients — whether it improves predictions of risk and the quality of information clinicians provide, and whether it changes patients’ treatment.
“We also need to know if it would be cost effective. The app we have designed needs to be evaluated in a clinical trial or further research, but could help in routine care,” he noted.
IANS
