Sheila was a diabetic who had been struggling with hypertension for years. She had been managing her condition with medication, but still found herself experiencing shortness of breath, fatigue, and swollen ankles. Finally, she went to her doctor and was diagnosed with heart failure – a condition where the heart is unable to pump enough blood to meet the needs of the body. It's a scary diagnosis, but for Sheila, it was the beginning of a new era of personalized treatment.
A new study published in the Lancet Digital Health journal has identified five subtypes of heart failure using machine learning. The study used electronic health records (EHR) from over 11,000 patients with heart failure across six hospitals in China. The patients were split into training and testing datasets to create the AI model, which was then validated using an independent dataset of over 1,000 patients. The AI identified five subtypes of heart failure, which the researchers believe will allow them to predict future risk for individual patients more accurately.
The five subtypes of heart failure identified by the AI are:
Each subtype has distinct clinical characteristics and outcomes, which the researchers say will allow doctors to tailor treatments to individual patients. For example, patients with subtype 1 may benefit from blood pressure medication, while those with subtype 2 might need medications to improve their heart function.
But why is this important?
Heart failure is a complex condition with many different possible causes. Until now, doctors have used a one-size-fits-all approach to treatment, meaning that patients may receive medications that aren't optimal for their specific subtype of heart failure. This new research suggests that by identifying subtypes of heart failure, doctors can provide more personalized treatment plans that are more effective for individual patients.
For example, a patient with HFpEF may have different symptoms and require different medications than a patient with HFrEF. The AI-identified subtypes allow doctors to identify the specific subtype of heart failure a patient has, which can then guide treatment decisions.
One of the most exciting things about this research is that it has the potential to impact millions of people. Heart failure affects an estimated 26 million people worldwide, and is responsible for significant healthcare costs and reduced quality of life.
By providing personalized treatment plans, this research could improve outcomes for heart failure patients around the world. It could also reduce healthcare costs, by allowing doctors to avoid prescribing medications that may not be effective for a specific patient's subtype of heart failure.
But what does this mean in real-world terms? Here are a few quantifiable examples:
As we've seen, the identification of AI-identified subtypes of heart failure has the potential to transform treatment for millions of people around the world. Here are three key takeaways from this research:
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Curated by Team Akash.Mittal.Blog
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