The advantages of technology at the service of health, patents in countless projects. This time it proves an algorithm whose specific purpose is to identify the risk of sudden death. This is revealed by a new study promoted by the Engineering Research Institute of Aragon (I3A) of the University of Zaragoza and led by the researcher Julia Ramírez. The new algorithm specifically associates variations in one of the parameters measured in the electrocardiogram (ECG) with sudden cardiac death. This, according to Ramírez, is “simple” to measure: only a resting ECG is needed, while other specific algorithms for sudden death require specific tests and, therefore, they are not so easy to use in clinical practice. In addition, until now the algorithms that existed did not distinguish the risk of sudden death well from other causes of death.
Considering that to date the algorithms that existed did not distinguish the risk of sudden death well from other causes of death, this is an important step forward in the health sector in general and in the diagnosis of patients in particular. In this sense, the aforementioned research work led by Ramírez emphasizes the shape of one of the ECG waves, called T-Wave. Thanks to its advances, for the first time waveform changes in a patient at rest have been evaluated with respect to an electrocardiogram of a healthy person.
In order to materialize this advance used data from the UK Biobank. This includes genetic and health information from more than half a million participants. Thus, it has been possible to evaluate 60,000 people without apparent risk, aged between 45 and 70 years. In addition, they have counted a second group, from the ARTEMIS study, made up of 2,000 Finnish people with coronary disease and an average age of 65 years. The participants were analyzed blindly without knowing a priori data of those who could have had a sudden death. In both population groups, the proposed index was specifically associated with the risk of sudden cardiac death. “These findings indicate a strong potential, as their algorithm could also be easily integrated into smart watches and mobile devices and detect these variations in the T-wave of the ECG.”, explains Ramirez.