Breakthrough Computational Method Predicts Sudden Cardiac Death

Unfortunately, sudden cardiac death is often the first indication of heart disease, and it can strike even young, seemingly healthy individuals, particularly during intense physical activity. To facilitate preventive measures, accurately assessing the risk of sudden death is crucial. Current consumer devices like smartwatches, which measure heart rate, possess the basic technical capabilities needed for identifying such cardiac risk factors. However, the heart rate interval analyses currently utilized are not sufficiently accurate for this purpose. Historically, the risk of sudden death has been evaluated using parameters from stress tests, including cardiorespiratory fitness and recovery heart rate tests. Cardiorespiratory fitness measures an individual's efficiency in transporting oxygen to the muscles and the muscles' ability to use oxygen during exercise. Now, a new computational approach has been developed that can estimate the risk of sudden cardiac death based on a one-minute heart rate measurement taken at rest.

Researchers at Tampere University (Tampere, Finland) have found that the new computational method they developed significantly improves the prediction of long-term sudden death risk. The assessment requires only a minute's worth of heartbeat intervals measured while at rest. This method draws on data from stress tests conducted as part of the Finnish Cardiovascular Study (FINCAVAS) project, which involved approximately 4,000 patients. It utilizes time series analysis to examine the dependencies of heart rate intervals and other complex indicators typical of various heart diseases over different time scales. Patients identified by this new method as having abnormal heart rate variability had a notably higher rate of sudden death compared to those with normal heart rate patterns, even when controlling for other risk factors.

This method holds considerable promise for pre-diagnosis and for identifying patients at high risk. It is independent of other measurements and could easily be integrated into devices such as smartwatches or smart rings. Research and development of this method are being expanded, using extensive databases on different heart conditions, with the goal of not only reliably detecting overall risk but also diagnosing common heart diseases like heart failure, which are currently challenging to identify with existing methods. The initial findings are very encouraging.

“It is possible that in many previously asymptomatic individuals, who have suffered sudden cardiac death or who have been resuscitated after sudden cardiac arrest, the event would have been predictable and preventable if the emergence of risk factors had been detected in time,” said Jussi Hernesniemi, Professor of Cardiology and lead author of the study.

“The most interesting finding of the study is the identification of differences specifically during measurements at rest. The characteristics of heart rate intervals of high-risk patients at rest resemble those of a healthy heart during physical exertion,” added doctoral researcher Teemu Pukkila.

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