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Friday, April 12, 2024

New AI Tool Is Better at Spotting Heart Failure Than Current Tests

A new artificial intelligence (AI) tool can help diagnose acute heart failure more accurately than current blood tests alone, according to research we’ve funded and published today in The BMJ. The researchers hope that the tool could help doctors to spot this type of heart failure sooner and improve patient care.

Researchers from the University of Edinburgh have developed a new tool which uses AI to combine routinely collected patient information with results from a blood test for the protein NT-proBNP, which is made by the heart. The tool – called CoDE-HF – produces an estimate of whether a patient has suffered heart failure.

More accurate than blood tests alone

They found that it could identify acute heart failure more accurately than blood tests for the pro-BNP protein and was especially precise in difficult to diagnose patient groups – such as older people and those with pre-existing medical conditions. The team hope that their tool can help to inform doctors’ decision making and improve care for patients with acute heart failure.
Acute heart failure affects nearly one million people in the UK and accounts for five percent of all unplanned hospital admissions. It is a life-threatening condition caused when the heart is suddenly unable to pump blood around the body.
However, diagnosis is difficult because symptoms, such as shortness of breath and leg swelling, occur in many other illnesses. Previous research has shown that patients who are diagnosed quickly benefit the most from treatment.
The current recommendation for diagnosing acute heart failure is a blood test to test to see if levels of NT-proBNP are below a certain level. However, this is not widely used as levels can vary depending on an individual’s age, weight and other health conditions.
To develop the tool the team, which included researchers in 14 countries, combined data from 10,369 patients with suspected acute heart failure. They are currently conducting further studies to understand how this decision-support tool will work in the hospital environment and influence patient outcomes.

Delivering more personalised care

Professor Nicholas Mills, British Heart Foundation Professor of Cardiology at the University of Edinburgh and Consultant Cardiologist, said:
“The application of artificial intelligence in decision-support tools as CoDE-HF to deliver more personalised care is particularly important given our ageing patient population who are living longer with more pre-existing medical conditions. We are currently conducting further studies to identify ways to implement CoDE-HF effectively in routine care.”
Professor Sir Nilesh Samani, our Medical Director, said:
“Early detection of acute heart failure is vital to improve outcomes, but in practice accurate diagnosis is challenging, because the main symptom – breathlessness – can be caused by other conditions. The new tool developed in this study identifies people with acute heart failure more accurately, allowing patients to get the lifesaving treatment they need sooner.”

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