ARTIFICIAL intelligence is more reliable in diagnosing acute heart failure than current blood tests alone, according to new research.

A study of more than 10,000 patients led by experts at Edinburgh University found that doctors could spot the condition sooner if they used an AI tool, known as CoDE-HF, which combines routine patient data with the results of a test which checks for elevated levels of a certain protein associated with heart failure.

Dr Ken Lee, cardiology specialist registrar and clinical lecturer at Edinburgh University, said: “Heart failure can be a very challenging diagnosis to make in practice.

“We have shown that CoDE-HF, our decision-support tool, can substantially improve the accuracy of diagnosing heart failure compared to current blood tests.”

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Previous research has shown that patients who are diagnosed quickly benefit the most from treatment.

Acute heart failure affects nearly one million people in the UK and accounts for five per cent of all unplanned hospital admissions. The prevalence is projected to rise by approximately 50% over the next 25 years owing to the ageing population.

It is a sudden, life-threatening condition caused when the heart is suddenly unable to pump enough oxygen-rich blood around the body to meet its needs.

It can be brought on by coronary heart disease – where the arteries become blocked, limiting blood flow – or by other ongoing conditions such as diabetes which damage cardiac tissue.

Diagnosis can be difficult because symptoms, such as shortness of breath and leg swelling, occur in many other illnesses.

Although many models have been developed to predict prognosis in patients with heart failure, very few have been developed to aid in the diagnosis of acute heart failure

Currently, both national and international guidelines recommend natriuretic peptide testing for diagnosis.

This determines whether to assess levels of a protein called NT-proBNP are below a certain threshold.

However, it is not widely used due to concerns that results can be skewed by factors such an individual’s age, weight and other health conditions.

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Researchers from Edinburgh University and 13 other countries combined data from 10,369 patients with suspected acute heart failure to develop the CoDE-HF tool to inform clinicians’ decisions.

CoDE-HF uses AI to combine routinely collected patient information with results from natriuretic peptide testing to produce an estimate of whether they suffered heart failure.

As well as spotting acute heart failure more accurately than heart protein blood tests on their own, CoDE-HF was especially precise in difficult to diagnose patient groups – such as older people and those with pre-existing medical conditions.

The team is currently conducting further studies to understand how this decision-support tool will work in the hospital environment and influence patient outcomes.

The research was funded by the British Heart Foundation (BHF) and the findings have been published in the BMJ journal.

Mr Dimitrios Doudesis, a research fellow and data scientist at

Edinburgh University, said artificial intelligence “has major potential to help doctors deliver more personalised patient care”.

NHS Grampian has already participated in a study looking at how AI can be used to scrutinise mammograms for signs of breast cancer, potentially relieving

radiologist and radiographer shortages and speeding up the turnaround time for results.

AI software is also being trialled at hospital sites across the north of Scotland to scan X-rays for lung cancer and detect things too small to be picked up by humans, while a pilot project in North Lanarkshire has increased capacity by around 25% by using AI to help clinicians to more rapidly identify abnormal cells in digital images of human tissue.

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The World Health Organisation said last year that AI “holds enormous potential for improving the health of millions of people around the world”.

Professor Nicholas Mills, a consultant cardiologist and BHF Professor of Cardiology at Edinburgh University, 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, medical director at the BHF, added: “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.”