Researchers have utilized machine learning to classify heart failure into five subtypes with differing mortality rates, thereby enhancing disease progression prediction. The team also developed a potentially useful app that can identify a patient’s heart failure subtype, which may improve treatment strategies and patient-clinician discussions.
A new study led by researchers at UCL (University College London) has identified five distinct heart failure subtypes, which could possibly be used to predict individual patients’ future risk levels.
Heart failure is a broad term denoting the heart’s inadequacy to effectively circulate blood throughout the body. However, current classification methods do not accurately predict how the disease is likely to progress.
A study recently published in Lancet Digital Health analyzed detailed anonymized data from over 300,000 individuals aged 30 and above diagnosed with heart failure in the UK within a 20-year span. By employing various machine learning techniques, the researchers delineated five distinct subtypes of the disease: early onset, late onset, atrial fibrillation related (a condition that causes irregular heart rhythm), metabolic (associated with obesity but exhibiting a low incidence of cardiovascular disease), and cardiometabolic (associated with both 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.
Lead author Professor Amitava Banerjee (UCL 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 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 datasets were Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN), covering the years 1998 to 2018.)
The research team trained the machine learning tools on segments of the data and, once they had selected the most robust subtypes, they validated these groupings using a separate dataset.
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.
Reference: “Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study” by Amitava Banerjee, Ashkan Dashtban, Suliang Chen, Laura Pasea, Johan H Thygesen, Ghazaleh Fatemifar, Benoit Tyl, Tomasz Dyszynski, Folkert W Asselbergs, Lars H Lund, Tom Lumbers, Spiros Denaxas and Harry Hemingway, 24 May 2023, The Lancet Digital Health.
DOI: 10.1016/S2589-7500(23)00065-1
The study was supported by the BigData@Heart Consortium from the European Union Innovative Medicines Initiative-2.
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