AI Doctor: AI tools found five forms of heart failure
AI Doctor: A new study conducted by UCL researchers has discovered five kinds of heart failure that might possibly be used to predict future risk for individual patients.
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.
As per the researchers 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%), late onset (46%), atrial fibrillation related (61%), metabolic (11%), and cardiometabolic (37%).
The research 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.
"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 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."
To avoid bias from a single machine learning method, the researchers used four separate methods to group cases of heart failure. They applied these methods to data from two large UK primary care datasets, which were representative of the UK population as a whole and were also linked to hospital admissions and death records.
The subtypes were established on the basis of 87 (of a possible 635) factors including age, symptoms, the presence of other conditions, the medications the patient was taking, and the results of tests (e.g., of blood pressure) and assessments (e.g., of kidney function).