Scientists have leveraged machine learning to categorize heart failure into five unique subtypes, each with varied mortality rates. This classification enhances the prediction of disease progression. The team also created a practical app to determine a patient’s heart failure subtype, potentially improving treatment methods and facilitating patient-doctor conversations.
The study, led by researchers from University College London (UCL), recognized five specific heart failure subtypes that may help predict the future risk for individual patients. Heart failure refers to the inability of the heart to efficiently distribute blood throughout the body. However, current classification techniques fail to precisely anticipate disease progression.
A recent publication in Lancet Digital Health, the study scrutinized comprehensive anonymized data from over 300,000 UK residents aged 30 or above who were diagnosed with heart failure over a two-decade period. Using a variety of machine learning methodologies, the team identified five distinct subtypes: early onset, late onset, atrial fibrillation-related, metabolic, and cardiometabolic.
The research disclosed varying mortality risks among the subtypes within the first year following diagnosis. The overall mortality risks at one year were: early onset (20%), late-onset (46%), atrial fibrillation related (61%), metabolic (11%), and cardiometabolic (37%).
Furthermore, the researchers devised an app, which can help healthcare providers determine a patient’s heart failure subtype. This could enhance predictions of future risk and guide patient conversations.
Professor Amitava Banerjee, the lead author from the UCL Institute of Health Informatics, emphasized the goal of refining heart failure classification to better comprehend and communicate disease progression. He pointed out the potential of improved disease classification to inform more specific treatments and stimulate new thinking around potential therapies.
The study employed four separate machine learning methods to classify heart failure, applied to data from two comprehensive UK primary care datasets. These datasets were representative of the UK population and linked to hospital admissions and death records.
The machine learning tools were trained on sections of the data, and once the most robust subtypes were determined, they were validated using a separate dataset.
The subtypes were determined based on 87 factors (out of a possible 635) including age, symptoms, co-existing conditions, medications in use, and results of tests and assessments. The team also examined genetic data from 9,573 individuals with heart failure from the UK Biobank study, finding a correlation between specific subtypes of heart failure and higher polygenic risk scores for conditions like hypertension and atrial fibrillation.
The research, “Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study”, was published in The Lancet Digital Health on May 24, 2023. The study received support from the BigData@Heart Consortium under the European Union Innovative Medicines Initiative-2.
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Table of Contents
Frequently Asked Questions (FAQs) about Machine Learning in Heart Failure Classification
What is the purpose of the heart failure subtype study led by researchers from UCL?
The purpose of the study is to categorize heart failure into five distinct subtypes using machine learning. This new classification may enhance the prediction of disease progression, facilitate patient-doctor discussions, and potentially improve treatment strategies.
What are the five subtypes of heart failure identified by the researchers?
The five identified subtypes of heart failure are early onset, late onset, atrial fibrillation-related, metabolic, and cardiometabolic. Each of these subtypes is characterized by different factors and exhibits distinct mortality rates.
What were the one-year mortality rates of the identified subtypes?
The overall mortality risks at one year were: early onset (20%), late-onset (46%), atrial fibrillation related (61%), metabolic (11%), and cardiometabolic (37%).
How did the researchers use machine learning in this study?
The researchers used machine learning to analyze comprehensive anonymized data from over 300,000 UK residents diagnosed with heart failure. They employed four separate machine learning methods to categorize heart failure, training these tools on sections of the data, and validating the identified subtypes using a separate dataset.
What is the potential use of the app developed by the researchers?
The app developed by the researchers can help healthcare providers determine a patient’s heart failure subtype. This could potentially improve predictions of future risk, guide conversations with patients, and inform more personalized treatment plans.
What future steps does the lead author of the study, Professor Amitava Banerjee, suggest?
Professor Banerjee suggests that the next steps should involve evaluating whether this new classification method can practically benefit patients by improving risk predictions, enhancing the quality of information clinicians provide, and potentially influencing treatment strategies. The app designed by the team also needs to be evaluated in a clinical trial or further research to establish its usefulness in routine care.
More about Machine Learning in Heart Failure Classification
- Heart Failure
- Lancet Digital Health Journal
- UCL Institute of Health Informatics
- Machine Learning in Healthcare
- UK Biobank Study
- European Union Innovative Medicines Initiative
- BigData@Heart Consortium
5 comments
OMG! Early onset heart failure has a 20% mortality rate in the first year?? That’s so scary!! This machine learning thing seems to be crucial in understanding such complexities in heart diseases.. bravo to these researchers!
Big fan of the use of machine learning here. This approach could really enhance patient care and treatment plans. Exciting times for medical research and practice. Looking forward to following the progress.
wow, that’s really something! its amazing to see how machine learning is transforming healthcare.. just hope the app theyve developed will actually work in real life and help people!
This could help so many people! It’s heartbreaking to see patients and families dealing with the uncertainty of heart failure. If this could give them some kind of roadmap…that would be huge!
Machine learning in healthcare is a booming field, and rightly so! Superb to see this application to classify heart failure, important stuff. Can’t wait to see how the app will turn out.