The model that allows us to know if a patient is going to survive Covid

The model that allows us to know if a patient is going to survive Covid

2023-05-26 12:48:30


The results of a first analysis carried out when the patient is admitted are sufficient to determine what the outcome of the disease will be.

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At the height of the pandemic, it was a real mystery why some patients in Covid they became ill with more virulence, and even died, sometimes having more favorable conditions than others who did not develop a serious clinical picture. After the global health alert ended, many studies have been carried out on the coronavirus and its treatment and detection have been improved. Today, it can even be predicted how a patient will develop the disease.

This is possible thanks to a model that, based on the statistical analysis of clinical results of Covid patients, researchers from the University of Alicante (AU). This tool makes it possible to predict the outcome of Covid in each person.

The results of this study have been published by the scientific journal Hellion and they are the basis for the development of a mobile application (app) that could be used in the future if similar circumstances arose, sources from the academic institution reported this Friday.

The professor Carlos Frutos Marhuendafrom the Department of Agrochemistry and Biochemistry of the UA, is responsible for this research, which “has been possible thanks to the database of the HM hospitals in Madridwhich left it open for scientific purposes”, according to the same sources.

Marhuenda explained that, by analyzing these clinical results of Covid patients, it was possible to assess which parameters were most important in order to predict the development of the disease.

He has pointed out that, with the results obtained from a first analysis carried out when the patient is admitted to the emergency services, they were able to detect or determine what the outcome of the disease would be.

“We can predict with a hit rate of more than 95% if the patient was going to die or was going to overcome the disease”, he assured.

According to Marhuenda, “this model could have been a very useful tool for medical personnel during the most difficult moments of the pandemic, as it would have helped make clinical decisions based on that information.”

“For example, in men, age was decisive as a risk factor, while in women, age was by no means the key factor in predicting the outcome of the disease,” said this researcher, who now has put his efforts into developing, thanks to this study, an app which, with a simple and intuitive use, could be used in the event of another pandemic.

“We wanted to create a predictive model to assess gravity and find out where it was going to evolve. To do this, we selected a series of factors from the analytics of about 300 patientsfrom his admission until the end of the development of his disease, and the result was very good, reaching 95% accuracy in the prediction”, indicated Marhuenda.

The model “was not used during the pandemic, but now, converted into an app, it could be very useful if similar circumstances occur,” he pointed out.

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