AI helps predict five subtypes of heart failure, study shows

According to a recent study, five subtypes of heart failure that could potentially pose a risk to individual patients have been identified using artificial intelligence (AI) tools. The study, published in The Lancet Digital Health journal, said that machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods.

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In the study, researchers looked at detailed anonymised patient data frompeople aged 30 years or older who were diagnosed with heart failure in the United Kingdom (UK) over a span of 20 years.

"Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset," the study added.

The five heart failure subtypes

The study identified five incident heart failure subtypes: (1) early onset, (2) late onset, (3) atrial fibrillation-related, (4) metabolic, and (5) cardiometabolic. The researchers evaluated subtypes for external validity (across datasets); prognostic validity (predictive accuracy for 1-year mortality); genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms.

Late-onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity. And 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).

Failure cluster app

The researchers also developed an app that could be used by clinicians to identify the cluster which a particular patient falls within, and their predicted survival. The clinicians interviewed felt the app was a feasible use of the identified clusters to identify which cluster a patient belonged to during consultations in routine care and that it could enable testing of effectiveness and cost-effectiveness in appropriately designed, prospective studies.

Professor Amitava Banerjee from University College London (UCL), who is the lead author of the study, said, "We sought to improve how we classify heart failure, with the aim of better understanding the likely course of the disease and communicating this to patients. Currently, how the disease progresses is hard to predict for individual patients."

Professor Banerjee pointed out that 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."

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