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Exciting New Tech Creates Potential for Early Warning Signs of AFib

Exciting news for those living with atrial fibrillation (AFib)! A recent study published in the journal Patterns shows promise for a new way to predict AFib episodes up to 30 minutes before they occur [1]. This research could further inform how we manage AFib and potentially improve patient outcomes.

What is Atrial Fibrillation?

AFib is the most common type of heart rhythm disorder, affecting millions worldwide. It occurs when the upper chambers of the heart (atria) beat irregularly and out of sync with the lower chambers (ventricles). Episodes of AFib may come and go; they can be uncomfortable for some people, they can be debilitating for others, and yet others still may have no symptoms at all.

Having AFib increases risk of several significant health complications, including stroke, heart failure, and dementia, so early identification and diagnosis is important so that these risks can be assessed and relevant treatment strategies put in place.

The Unpredictability of AFib Episodes has an impact on people’s lives

Atrial Fibrillation is usually only detected during an episode – it’s usually when people have symptoms and then are able to access and record an ECG tracing. Episodes can come and go unpredictably, and in those who are significantly affected by the symptoms of AFib, this can have an impact on their life and their confidence in going about normal activities. It can also mean that they have to take medication every day to prevent episodes (these are called ‘anti-arrhythmic’ medications). Sometimes these medications can have side effects as well.

The Power of Smartwatch Technology – warning people of a potential upcoming episode

This new study used wearable technology to continuously monitor heart health, and combined with a new artificial intelligence model developed by the researchers, has been able to predict the onset of an AFib episode up to 30 minutes prior to the episode occurring. The WARN model (Warning of Atrial fibRillatioN) could enable new approaches to managing previously unpredicable episodes.

WARN uses variations in heart rate (specifically, the time between heartbeats) to identify patterns that indicate an impending AFib episode. This information is then used to calculate the likelihood of AFib occurring. If an episode is likely, by giving patients an early warning, they could potentially take steps to prevent or better manage the episode.

The Future of AFib Management

The researchers suggest that having access to WARN could enable new approaches to managing AFib episodes. For example, it might mean that certain types of anti-arrhythmic medications could be given just at the warning prior to an episode, to prevent it arising. It may reduce the need for ongoing daily antiarrhythmic medications.

Furthermore, the researchers suggest that personalised models based on long-term individual heart rhythm data could continuously learn and adapt based on an individual’s heart rhythm patterns. This personalised approach could lead to even earlier and more accurate AFib predictions.

Looking Ahead

While further research is needed, this study offers a glimpse into a future where AFib management is more proactive. Early detection of AFib episodes could lead to better patient outcomes and improved quality of life.

At the Atrial Fibrillation Institute, we are passionate about staying at the forefront of AFib research and treatment. We are excited about the potential of this new technology and look forward to seeing how it unfolds.

Learn More:

R[1] Gavidia et al., Early warning of atrial fibrillation using deep learning, Patterns (2024), https://doi.org/10.1016/j.patter.2024.100970