Finding a Way to Predict COVID-19 Severity in Kids

Children with severe SARS-CoV-2 infection had significantly elevated levels of a trio of saliva cytokines, according to a preliminary analysis.

In the pilot study with 180 children, those with severe COVID-19 had elevated levels of CXCL10, MIG, and TNF R-1 in their saliva versus children with mild COVID-19, reported Usha Sethuraman, MD, of Central Michigan University in Mount Pleasant, at the American Academy of Pediatrics (AAP) virtual meeting.

While specific cytokine profiles could not be related to various symptoms of COVID-19 infection — partly due to small sample size — the researchers hope additional data will enable them to differentiate the biomarkers of pediatric COVID-19 patients, she noted.

“A lot of these children who have COVID have very similar symptoms to those with other common viral infections,” Sethuraman told MedPage Today. “And there is no way for us to tell which child is going to get sicker or not. So if we have a method to determine which child is going to develop [multisystem inflammatory syndrome (MIS-C)] or severe COVID, that would help us start treatment earlier.”

“So in that sense, this would be a game changer,” she added.

COVID-19 cases in children are generally mild, but some younger patients can present with MIS-C, Kawasaki disease, or respiratory failure. In the 24 states and New York City that report on child hospitalizations, children are hospitalized in 0.1%-1.9% of all pediatric COVID-19 cases, according to September 2021 AAP data.

Currently, there are no established biomarkers that can predict the progression and severity of COVID-19 in children, although studies have linked disease severity with enhanced cytokine levels in adults.

Sethuraman said her group chose saliva cytokines because of the ease of sampling. “The primary purpose of this study was to make it as noninvasive as possible,” she said.

The study started in March 2021 and will go until June 2022. Initial data from March until May came from the Children’s Hospital of Michigan in Detroit and UPMC Children’s Hospital of Pittsburgh. The analysis of saliva samples is being done at the Penn State College of Medicine in Hershey, Pennsylvania, while model development using artificial intelligence (AI) is being done at Wayne State University in Detroit.

Of the 180 children (mean age 7.1 years; 49.9% female) enrolled in the study thus far, 60 were hospitalized and 40 were categorized as having severe COVID-19. Of those 40, five exhibited cardiac symptoms, 26 had severe respiratory symptoms, while the others exhibited neither, but “were mostly diagnosed with MIS-C,” according to Sethuraman.

Six salivary cytokines — TNF R-1, IL-13, IL-15, CCL7, CXCL10, and MIG (CXCL9) — were measured, and all three of the elevated cytokines in patients with severe disease were pro-inflammatory, the researchers reported.

They also found that cytokines did not vary significantly (P>0.05) between various COVID-19 phenotypes, and that a hierarchical logistic regression showed that a three-cytokine-based model accounted for “only 4.2% of the variance between severe and non-severe COVID groups.”

“The combined model of the three cytokines with age, gender and asthma status showed moderate accuracy (75%) and poor sensitivity (21%),” the researchers stated, and that was a study limitation, along with the small number of patients.

Sethuraman and colleagues also measured miRNA levels in 129 saliva samples. They found that levels of 63 miRNAs differed significantly between severe and nonsevere cases in their initial analysis, and showed promise as a predictive model.

“Our ultimate goal is to incorporate all features, including social determinants of health and clinical information, and use AI to make a model,” Sethuraman said. “[We want] to narrow down to a few miRNA and some cytokines, and develop a bedside tool [that] spits out a prediction for us for whether a child has severe disease.”