Artificial intelligence has been trained to identify those at risk of developing atrial fibrillationTelegraph Reporters

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• Artificial Intelligence, 
• Health news, 
• Heart disease, 
• NHS (National Health Service), 
• Leeds, 
• West Yorkshire

28 December 2024 7:47am GMT

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Artificial intelligence is being used to find people with heart conditions before they even have symptoms.

In an ongoing trial, a ground-breaking tool scours GP records for “red flags” which could indicate whether a patient was at risk of developing atrial fibrillation (AF).

John Pengelly, a former Army captain, said he was “really grateful” that his AF risk had been detected by the algorithm. He now takes a “couple of pills a day” to reduce his heightened chance of a potentially deadly stroke.

AF causes an irregular and often abnormally fast heart rate, and people with it have a significantly higher risk of stroke. 

Some sufferers experience heart palpitations, dizziness, shortness of breath and tiredness – but others have no symptoms and are unaware their heart rate is irregular.

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Around 1.6 million people in the UK have been diagnosed with AF but the British Heart Foundation (BHF) said there were probably many thousands of undiagnosed cases in the UK.

When AF is identified and treated early it can be managed and the stroke risk reduced.

The new AI tool is being assessed in a trial, called Find-AF, funded by BHF and the Leeds Hospitals Charity.

Its algorithm was developed by scientists and clinicians at the University of Leeds and Leeds Teaching Hospitals NHS Trust, with funding from the BHF.

They trained the AI tool using anonymised electronic health records of over 2.1 million people, training the algorithm to find warning signs that could indicate a person is at risk of developing AF.

The tool was validated with medical records from a further 10 million people and is now  examining GP records at several surgeries in West Yorkshire.

Experts are assessing how effective it is at finding people who are at risk of developing AF in the next six months, with those identified then offered further testing.

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The software works out someone’s risk based on a number of factors including age, sex, ethnicity and whether or not they have other medical conditions including heart failure, high blood pressure, diabetes, ischaemic heart disease and chronic obstructive pulmonary disease.

Those identified as high risk are offered a handheld electrocardiography (ECG) machine to measure their heart rhythm twice a day for four weeks, as well as any time they feel heart palpitations.

If the ECG machine readings indicate that a patient has AF, their GP is informed and they can discuss treatment options.

Experts hope that the West Yorkshire study will pave the way for a UK-wide trial, which could prevent a number of avoidable strokes – estimates suggest that AF is a contributing factor in around 20,000 strokes every year in the UK.

Chris Gale, professor of cardiovascular medicine at the University of Leeds and honorary consultant cardiologist at Leeds Teaching Hospitals NHS Trust, said: “All too often, the first sign that someone is living with undiagnosed AF is a stroke.

“This can be devastating for patients and their families, changing their lives in an instant.

“It also has major cost implications for health and social care services – costs which could have been avoided if the condition were spotted and treated earlier.”

Atrial Fibrillation

What are the signs – and how is it treated?

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Dr Sonya Babu-Narayan, associate medical director at the British Heart Foundation and consultant cardiologist at Royal Brompton Hospital, said: “By harnessing the power of routinely collected healthcare data and prediction algorithms, this research offers a real opportunity to identify more people who are at risk of AF and who may benefit from treatment to reduce their risk of a devastating stroke.”

Dr Ramesh Nadarajah, from Leeds Teaching Hospitals NHS Trust, said: “Data are collected about patients in every interaction they have with the NHS.

“These data have huge potential to make early identification of and testing for conditions like AF easier and more efficient.”

Earlier in December, Wes Streeting, the Health Secretary, said  AI and big data were “game-changing” for healthcare.

He told the Health and Social Care Committee: “We can use AI, machine learning, genomics, big data, to not only intervene early with earlier diagnosis and earlier treatment, but to actually predict and prevent illness, which is the game changing paradigm shift in healthcare in this century.”