Artificial Intelligence identifies five subtypes of heart failure
A new study, led by an Indian-origin researcher and published in Lancet Digital Health, has identified five subtypes of heart failure that could potentially be used to predict future risk for individual patients using artificial intelligence (AI) tools. 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.
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, metabolic, and cardiometabolic.
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 clinics 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.
Lead author Professor Amitava Banerjee from UCL’s Institute of Health Informatics said, “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. 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.”
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 clinics provide, and whether it changes patients’ treatment.
This study is significant because it demonstrates how AI can be used to identify specific subtypes of heart failure that can help clinicians to predict the course of the disease and ultimately provide more personalized treatments. Heart failure is a serious condition that affects millions of people worldwide and is a leading cause of hospitalization and death. By identifying specific subtypes of heart failure, clinicians can provide more targeted treatments and improve patient outcomes.
In conclusion, this study is an important step towards improving the diagnosis and treatment of heart failure and highlights the potential of AI to transform healthcare. As AI continues to advance, we can expect to see more studies like this that use machine learning to identify specific subtypes of diseases and provide personalized treatments.
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News Source : IANS
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