of sudden cardiac death is often the last, so being able to accurately predict the risk of this event from a simple heart rate measurement could save many lives. The method is based on the variability of the heart rate and has been tested on a large dataset of over 4,000 patients. The results showed that the method was able to accurately identify individuals at risk of sudden cardiac death, which could potentially lead to early interventions and prevention strategies. This innovative approach could significantly improve the ability to identify and prevent sudden cardiac death, ultimately saving lives.
Sudden cardiac death is a common result of heart disease and can occur unexpectedly, even in young and seemingly healthy individuals, particularly during intense physical activity. It is crucial to accurately assess the risk of sudden death in order to administer preventive treatment. While consumer devices like smart watches can measure heart rate and have the potential to determine cardiac risk factors, current heart rate interval analyses are not precise enough for this purpose. Previous studies have used parameters measured during a stress test to evaluate the risk of sudden death.cardiorespiratory fitness and recovery heart rate were subjected to tests. Cardiorespiratory fitness refers to the body’s ability to deliver oxygen to muscles and the muscle tissue’s capacity to utilize oxygen during physical activity.
Researchers at Tampere University have created a new computational method that offers a much more accurate prediction of the long-term risk of sudden death. This assessment can be done using only the intervals between heartbeats measured during one minute of rest. The discovery is based on stress test data gathered from around 4,000 patients participating in the Finnish Cardiovascular Study (FINCAVAS) project.
Patients withAbnormal heart rate variability detected using the new method was linked to a higher incidence of sudden death compared to patients with normal heart rate patterns. The analysis also took other risk factors into consideration.
This method shows potential for early diagnosis and identifying high-risk patients. It does not rely on other measurements and could easily be integrated into wearable devices such as smart watches or smart rings.
“It is possible that many individuals who were previously asymptomatic may have experienced sudden cardiac death or been at a high risk of it.Resuscitated after a sudden cardiac arrest, the occurrence could have been anticipated and prevented if the early detection of risk factors had been detected,” Jussi Hernesniemi, Professor of Cardiology and main writer of the report explained.
The new approach is grounded on time series analysis created by a research group in computational physics led by Professor Esa Räsänen. The analysis can be applied to examine the interconnections of heart rate intervals and other intricate characteristics of various heart diseases at varying time frames.
<p”The most fascinating discovery of the research is the recognition of variations particularlyWhile at rest, the heart rate intervals of high-risk patients mimic those of a healthy heart during physical activity, according to Teemu Pukkila, a doctoral researcher. The method’s development and research are ongoing and being expanded with the use of databases on various heart diseases. The goal is to accurately identify not just the overall risk, but also the most prevalent heart diseases, like heart failure, which are difficult to diagnose using current methods. The initial findings show great promise.