New Algorithm Helps Predict Sudden Cardiac Death
Researchers from Tampere University in Finland have found a new algorithm that helps in identifying abnormal cardiac rhythms linked to risks of heart failure. Sudden cardiac arrest is one of the leading causes of death and is responsible for approximately half of all deaths from cardiovascular disease across the globe.
A report in Science Alert says that the new method for identifying cardiac rhythms associated with imminent heart failure could one day buy precious time for those at risk. The study was published in JACC: Clinical Electrophysiology and said that the new algorithm makes use of a particular metric called detrended fluctuation analysis (DFA2 a1) which can help detect changes in heart rate variability over time.
Sudden cardiac arrest occurs when there is a sudden loss of all heart activity due to an irregular heart rhythm. Your breathing also stops and the person becomes unconscious. Without immediate treatment, the condition can lead to death.
Sudden cardiac arrest is not the same as a heart attack. A heart attack happens when blood flow to a part of the heart is blocked. Sudden cardiac arrest is not due to a blockage. However, a heart attack can cause a change in the heart’s electrical activity that leads to sudden cardiac arrest, says Mayo Clinic.
The study was conducted on 2,794 adults over an average follow-up period of 8.3 years. The researchers found that DFA2 a1 is a “powerful and independent predictor” of sudden cardiac death (SCD). The association is strongest when the body is at rest, rather than engaging in physical activity.
Teemu Pukkila, a physicist at Tampere University said, “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.”
The researchers used statistical analysis methods to connect DFA a1 patterns to SCD incidents. This included factoring in the impact of other important variables, including age and existing heart health conditions.
The researchers in their published paper, “Accelerometers in wearable consumer devices can easily distinguish between the states of physical activity and rest and perform the measurement when applicable.”
This new algorithm is more accurate than the method that’s used now. This is because the current methods include measuring cardiorespiratory fitness; which means someone’s capacity to send oxygen to the muscles and the extent to which those muscles can use the oxygen during physical exercise.
The next steps are to test the approach with larger and more diverse groups of people and to see how the findings might relate to other types of heart disease too. Ultimately, the predictive algorithm could end up saving a substantial number of lives, by warning those at risk from this sudden and quick killer.
Jussi Hernesniemi, cardiologist from Tampere University said, “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.”
