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Five Types of Heart Failure Identified Using 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.
[...] 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 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."
[...] 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).
The team also looked at genetic data from 9,573 individuals with heart failure from the UK Biobank study. They found a link between particular subtypes of heart failure and higher polygenic risk scores (scores of overall risk due to genes as a whole) for conditions such as hypertension and atrial fibrillation.
Journal Reference:
Amitava Banerjee et al., Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study [open], Lancet Digital Health, 2023. DOI: https://doi.org/10.1016/S2589-7500(23)00065-1 [doi.org]
