A special study for rapid diagnosis of Covid-19-related inflammatory syndromes in children

The National Institutes of Health awarded eight research grants to improve new technologies
for the early diagnosis of severe illness caused by SARS-CoV-2 infection in children.
The new award follows a grant awarded in 2020 to develop a way to diagnose children at high risk for multisystem inflammatory syndrome (MIS-C), a rare, severe, and sometimes fatal sequelae of childhood infection with or exposure to SARS-CoV-2
.

While some children have mild or no symptoms of COVID-19, others experience more severe effects, including MIS-C, which causes inflammation of one or more organs, including the heart, lungs, kidneys, brain, skin, eyes, and gastrointestinal tract
.

"These highly innovative technologies and tools have the potential to dramatically improve care for children with SARS-CoV-2 infection and other fever-causing illnesses," said Bill Kapogiannis, MD, of the National Institutes of Health's Eunice Kennedy Schriver National Institute for Child Health and Human Development (NICHD), which oversees the grant
.

The awards come from NIH's "Using Laboratory Diagnostics and Artificial Intelligence to Predict the Severity of Viral-Associated Inflammatory Disease Severity in Children" (PreVAIL kIds) program
.
They are part of the Rapid Accelerated Essential Diagnostic Methods (RADx-rad) initiative, which aims to support new, non-traditional methods and the repurposing of existing tools to bridge gaps
in COVID-19 detection and surveillance.

The 2020 award supported research involving more than 7,400 study participants in four countries and produced prototype methods and technologies
that could be used for hospitalized patients in clinics, emergency departments and hospitals.
These "children" studies were supported by the NIH RADx-rad program as part of
a collaborative NIH effort called "Caring for Children with COVID.
" The results of these studies include a laboratory technique to detect specific immune cells associated with MIS-C – a database based on certain blood proteins and genetic biomarkers to help diagnose children at risk of MIS-C and severe COVID-19; and a database
that can distinguish between MIS-C, Kawasaki disease (with similar symptoms) and viral and bacterial infections that cause fever.

The new prize will enable researchers to continue their efforts to develop ways to rapidly diagnose MIS-C and identify those
at risk of severe and long-term effects of SARS-CoV-2.
Early identification of those most at risk will enable early intervention to prevent serious health effects
.

Winners:

• Jane C.
  Burns, University of California, San Diego, Diagnoses and predicts risk in children with SARS-CoV-2-related diseases

• Cedric Manlhiot, Johns Hopkins University, Data Science Methods Identify and Manage SARS-Related MIS-C

• Ananth V.
  Annapragada, Baylor College of Medicine, AICORE-kids: COVID-19 Risk Assessment for Artificial Intelligence in Children

• Audrey R.
  Odom John, Children's Hospital of Philadelphia, Diagnosis of MIS-C in febrile children

• Usha Sethuraman, University of Central Michigan, Severity predictor integrating salivary transcriptomics and proteomics with multineural network intelligence in children with SARS-CoV-2 infection

• Juan C.
  Salazar, Connecticut Children's Medical Center, Identifying biomarker signatures for the prognostic value of MIS-C

• Charles Yen Chiu, University of California, San Francisco, Discovery and clinical validation of COVID-19 disease severity and MIS-C host biomarkers

• Lawrence Kleinman, Robert Wood Johnson School of Medicine at Rutgers University, COVID-19 networks expand clinical and translational approaches to predict severe illness in children