Machine learning algorithm may help identify high risk atrial fibrillation patients

Global biopharmaceutical company Bristol Myers Squibb and Pfizer, have jointly developed an artificial intelligence (AI)-based machine learning (ML) algorithm to identify atrial fibrillation (AF) in high risk patients.

Results of the trial that proves the success and cost effectiveness of the new method were announced on Sunday (August 29) at ESC Congress 2021, organised by the European Society of Cardiology (ESC).

The PULsE-AI trial was conducted with 1,880 people aged 30 and over during a 20-month period to assess the efficacy of the method in primary care settings in the UK.

After being identified, the patients were then split into two groups – the intervention group and the control group.

The intervention group was invited to attend a research clinic for diagnostic testing, while the other group only had access to usual clinical practice for diagnosis of AF.

Following identification through the AI algorithm, 4.97 per cent of the intervention group, and 4.93 per cent of the control group were diagnosed with the condition. Therefore, the authors of the study concluded “the algorithm may be an effective tool in narrowing the population at high risk of undiagnosed AF who should undergo diagnostic testing.”

“We are pleased that these results confirm the effectiveness of the algorithm in helping to identify people at risk of AF in both the control and intervention groups,” said Usman Farooqui, Executive Director - Head of Medical Affairs, Central Eastern Europe, Turkey, Israel & India (CEETII) at Bristol Myers Squibb.

AF is one of the most prevalent cardiovascular conditions and a leading cause of stroke, heart failure, cardiovascular morbidity and sudden death. Its detection is difficult as some of the affected people experience minimal or no symptoms at all.

However, early detection and management can improve outcomes for patients.

Farooqui said: “We aim to share the PULsE-AI algorithm as a tool that could help health providers in the near future. When implemented in a clinical setting, it has the potential to identify more people who need AF management so they can receive the care that they need, when they need it.”

Further results of the health economic impact assessment will be shared later in the year.