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In a groundbreaking development, physicists at Tampere University have unveiled a new computational method capable of estimating the risk of sudden cardiac death (SCD) from just a one-minute heart rate measurement taken at rest. This significant advancement is the result of an interdisciplinary collaboration between the fields of cardiology and computational physics.
Sudden cardiac death often strikes without warning, even in young and seemingly healthy individuals, particularly during intense physical activity. The unpredictability of SCD has made it a challenging condition to manage. However, early detection of risk is crucial for organizing preventive treatments and potentially saving lives.
Traditional heart rate interval analyses have lacked the precision needed to predict SCD effectively. While consumer devices like smartwatches have the technical capability to measure heart rate, they haven’t been accurate enough to serve as reliable predictors of cardiac risk factors—until now.
In previous studies, SCD risk has typically been assessed using parameters derived from stress tests, such as cardiorespiratory fitness and recovery heart rate. Cardiorespiratory fitness measures the body’s ability to transport oxygen to muscles and use it during physical activity, offering insight into cardiovascular health.
The researchers at Tampere University, however, discovered that their new computational method offers a significantly improved estimate of the long-term risk of sudden cardiac death. Remarkably, this assessment only requires heartbeat interval data collected during one minute of rest. The study’s findings are based on data from the Finnish Cardiovascular Study (FINCAVAS) project, which involved approximately 4,000 patients.
Patients identified with abnormal heart rate variability through the new method exhibited a substantially higher incidence of SCD compared to those with normal heart rate characteristics. The analysis also accounted for other risk factors, enhancing the reliability of the predictions.
This innovative method holds great potential for pre-diagnosis and identifying high-risk patients. Notably, it is independent of other measurements and could be easily integrated into consumer devices like smartwatches and smart rings, making it accessible for widespread use.
“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 Professor Jussi Hernesniemi, a leading cardiologist and the study’s lead author.
The method is based on time series analysis developed by a computational physics research group led by Professor Esa Räsänen. This analysis examines the interdependencies of heart rate intervals and other complex properties characteristic of various heart diseases over different time scales.
“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,” explained doctoral researcher Teemu Pukkila.
The development and research of this method are ongoing, with expansion plans leveraging databases on different heart diseases. The goal is to reliably identify not only overall SCD risk but also common heart conditions like heart failure, which are currently challenging to diagnose with existing methods. Early results from these expanded studies are highly promising.
As this innovative method continues to be refined and integrated into everyday technology, it promises a future where sudden cardiac death is no longer a silent killer but a preventable condition.
