Health
Study Finds AI Can Help Predict Five Subtypes of Heart Failure
A recent study used artificial intelligence (AI) tools to identify five subtypes of heart failure that could potentially pose a risk to individual patients. the study, Published in The Lancet Digital Health JournalHe noted that while machine learning has been used to analyze subtypes of heart failure, it can be used to analyze large, individualized, population-based datasets, across the spectrum of causes and symptoms, or clinical and non-clinical studies through various machine learning techniques. It said it did not use clinical validation.
In this study, researchers examined 20 years of detailed anonymized patient data from people over 30 diagnosed with heart failure in the UK.
“Pre-HF and post-HF factors (n=645) included demographic information, medical history, laboratory tests, blood test values, medications. , hierarchical, K-Medoids), mixed-model clustering), using 87 of the 645 factors in each data set,” the study adds.
Five Heart Failure Subtypes
This study identified five heart failure subtypes: (1) early-onset, (2) late-onset, (3) atrial fibrillation-related, (4) metabolic, and (5) cardiometabolic. The researchers assessed the subtypes for external validity (across the dataset). Prognostic validity (prediction accuracy of 1-year mortality). Genetic plausibility (UK Biobank), association of heart failure-related traits with polygenic risk score (PRS) (n=11), and single nucleotide polymorphisms.
Late-onset and cardiometabolic subtypes were most similar and strongly associated with PRS of hypertension, myocardial infarction and obesity. The 1-year risk of all-cause mortality was premature (20%), late (46%), atrial fibrillation-related (61%), metabolic (11%), and cardiometabolic (37%). )Met. cents).
Cluster app failure
The researchers also developed an app that clinicians can use to identify the cluster a particular patient belongs to and their predicted survival. Clinicians interviewed said the app was viable and well-designed to take advantage of identified clusters to identify which cluster a patient belongs to during routine clinical visits. We felt that it might be possible to test efficacy and cost-effectiveness in a prospective study.
Lead author of the study, Professor Amitabha Banerjee of University College London (UCL), said: “We are looking at heart failure with the aim of better understanding and communicating the possible course of the disease to patients. We tried to improve the classification method,” he said. At this time, it is difficult to predict how the disease will progress for individual patients. “
Professor Banerjee noted that better differentiation between types of heart failure could lead to more targeted treatments and “may help us think differently about potential treatments.” .
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