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Many cases of sudden cardiac death could be avoided thanks to artificial intelligence. As part of a new study to be published in European Heart Journal, a network of artificial neurons imitating the human brain was developed by researchers from Inserm, Paris Cité University and the Paris public hospitals group (AP-HP), in collaboration with their colleagues in the USA. During the analysis of data from over 240 000 ambulatory electrocardiograms, this algorithm identified patients at risk of a serious arrhythmia that was capable of triggering cardiac arrest within the following 2 weeks in over 70% of cases.

Each year, sudden cardiac death is responsible for over 5 million deaths worldwide[1]. Many of these cardiac arrests occur out of the blue with no identifiable warning signs, striking individuals from the general population who do not always have a known history of heart disease.

Artificial intelligence could help to improve the anticipation of arrhythmias – unexplained heart rhythm disorders which, if severe, can cause fatal cardiac arrest – according to a new study led by a team of researchers from Inserm, Paris Cité University and the Paris public hospitals group (AP-HP), in collaboration with their colleagues in the USA.

As part of this study, a network of artificial neurons was developed by a team of engineers from the company Cardiologs (Philips group) in collaboration with the universities of Paris Cité and Harvard. What this algorithm does is imitate the functions of the human brain in order to improve the prevention of cardiac sudden death.

The researchers analysed several million hours of heartbeats thanks to data from 240 000 ambulatory electrocardiograms collected in six countries (USA, France, UK, South Africa, India and Czechia).

Thanks to artificial intelligence, the researchers were able to identify new weak signals that herald the risk of arrhythmia. They were particularly interested in the time needed to electrically stimulate and relax the heart ventricles during a complete cycle of cardiac contraction and relaxation.

“By analysing their electrical signal for 24 hours, we realised that we could identify the subjects susceptible of developing a serious heart arrhythmia within the next two weeks. If left untreated, this type of arrhythmia can progress towards a fatal cardiac arrest”, explains Dr Laurent Fiorina, first author of the study, researcher at the Paris Cardiovascular Research Centre (PARCC) (Inserm/Paris Cité University), cardiologist at Cardiovascular Institute Paris-Sud (ICPS) (Ramsay, Massy), and medical director in charge of artificial intelligence at Philips.

While the artificial neural network is still in the evaluation phase, it showed itself in this study to be capable of detecting at-risk patients in 70% of cases, and no-risk patients in 99.9% of cases.

In the future, this algorithm could be used to monitor at-risk patients in hospital. If its performances are refined, it could also be used in devices such as ambulatory Holters that measure blood pressure to reveal hypertension risks. It could even be used in smartwatches.

“What we’re proposing here is a paradigm change in the prevention of sudden death, comments Eloi Marijon, Inserm research director at PARCC (Inserm/Paris Cité University), professor of cardiology at Paris Cité University and head of the cardiology department at Georges Pompidou European Hospital AP-HP. Until now we’d been trying to identify patients at risk over the medium and long term, but were incapable of predicting what could happen in the minutes, hours or days that precede a cardiac arrest. Now, thanks to artificial intelligence, we can predict these events in the very short term and potentially take action before it’s too late.”

The researchers now wish to conduct prospective clinical studies to test the efficacy of this model under real-world conditions.

“It’s essential for this technology to be evaluated in clinical trials before being used in medical practice, insists Dr Fiorina. But what we’ve already shown is that AI has the potential to radically transform the prevention of serious arrhythmias.”

[1] https://www.thelancet.com/commissions/sudden-cardiac-death

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