Using Predictive Analytics to Determine COVID-19 Severity in Children
By testing saliva samples, researchers are using predictive analytics to identify children at risk of severe COVID-19.
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By - By examining the relationship between cytokines in saliva and COVID-19 infection, an ongoing study is researching how to use predictive analytics to determine the severity of infection.
In a preliminary analysis of saliva samples from 150 children, the researchers discovered that levels of two cytokines were higher in those with severe COVID-19 compared to those without. According to the researchers, the biomarkers could control the inflammation in the body once an individual has been infected with COVID-19 and could predict the severity of the infection.
“Using saliva to predict severity of the infection is non-invasive and painless. If proven to be effective saliva may be a game-changer in children in whom obtaining blood is both difficult and distressing. Additionally, early recognition of the severity of COVID-19 can help clinicians institute timely and appropriate treatment which may help improve outcomes.” study author Usha Sethuraman, MD, said in a press release.
Thus far, many children who have tested positive for COVID-19 have mild infections. However, with new variants, there is no guarantee that that will remain the case. Some children have developed severe complications such as respiratory failure or heart inflammation, according to researchers.
Cytokines are proteins found in blood and saliva that could produce a response to a COVID-19 infection. Studies in adults have demonstrated that certain cytokines are elevated in the blood of patients with COVID-19, and with predictive analytics, could determine the infection severity.
According to researchers, the study aims to develop a method to identify children at risk of severe COVID-19 by integrating biomarkers and social determinants of health using artificial intelligence. In addition, researchers are in the process of gathering saliva samples from 400 children ages 18 and young who have tested positive for COVID-19.
The patient data is coming from Children’s Hospital of Michigan and UPMC Children’s Hospital of Pittsburgh. The saliva sample analysis is being performed at Penn State College of Medicine, while Wayne State University is creating the model using artificial intelligence.
“In addition to finding that levels of two cytokines (MIG and CXCL-10) in the preliminary analysis were higher in those with severe COVID-19 compared to those without severe infection, dozens of microRNA levels were found to be altered, with the majority of them being significantly lower in the saliva of children with severe infection,” the press release stated.
Ongoing analysis will pursue the validation of the results and confirm the importance of saliva cytokines and microRNAs, combined with social factors, including where a child lives.
The study is supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development through the National Institutes of Health’s Rapid Acceleration of Diagnostics program (1R61HD105610).
