According to preliminary research carried out by specialists in France, it is suggested that the use of artificial intelligence (AI) and the analysis of information from electronic health records could allow the prediction of sudden cardiac death.
These researchers were also able to develop a personalized risk profile for each individual, with the potential to be used in the future to address individual risks before a person has a cardiac arrest, thereby helping to prevent deaths.
Artificial intelligence could provide new strategies for prevention and global health management, including the prediction of sudden cardiac death, according to preliminary research presented at the American Heart Association Scientific Symposium on Resuscitation Science, held Nov. 11-12 in Philadelphia. This meeting represents a premier global forum for the latest advances in the treatment of cardiorespiratory arrest and life-threatening traumatic injuries.
Sudden cardiac death represents between 10% and 20% of all deaths and it is difficult to predict the risk of this event, especially at an individual level, according to the statements of the doctor Xavier Jouven, lead author of the study, professor of cardiology and epidemiology at the Inserm Cardiovascular Research Center U970 of the University of Paris.
French researchers propose a new approach that does not focus only on common cardiovascular risk factors, but by integrating all available medical information in electronic health records. They analyzed the medical records of more than 25,000 people who died as a result of sudden cardiac arrest and another 70,000 people in the general population, comparing the data from the two groups by age, sex and location.
Using artificial intelligence, the researchers developed more than 25,000 personalized equations to assess each person’s risk of sudden cardiac death based on past medical information. They also created a personalized risk profile for each individual in the study.
These personalized risk equations included various medical details, such as treatment for high blood pressure and history of heart disease, as well as information about mental and behavioral disorders, such as alcohol abuse.
Analysis of the data identified the factors that were most likely to increase or decrease the risk of sudden cardiac death by a certain percentage and over a certain time period. For example, an 89% risk of sudden cardiac death within three months was identified.
Through AI, researchers were able to identify more than 90% of people at high risk of sudden cardiac arrest, representing more than a quarter of all cases.
However, the researchers point out that the medical data collected may have limitations, including indirect information or information completed by relatives or medical professionals, as well as differences between data collected in different countries, requiring adjustments for prediction models.
These findings are considered preliminary and require evaluation by the scientific community and publication in specialized medical journals.
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