dbGaP Study Accession: phs002609
NIH Institute/Center: NICHD
RADx Data Program: RADx-rad
Release Date: 11/07/2022
DOI: 10.60773/jcap-3849
Study Description: The purpose of this study was to develop a tool that can diagnose multisystem inflammatory syndrome in children (MIS-C.) Some children and young adults who have had COVID-19 develop this rare disease about a month after they recover. It can be serious, as it causes inflammation in various organs, including the heart, lungs, kidneys, brain, skin, eyes, and intestines. MIS-C has symptoms similar to several diseases, including COVID-19 and Kawasaki disease, making diagnosis difficult. Currently, patients must undergo a battery of tests in order to diagnose MIS-C, including bloodwork, x-rays, and echocardiograms, which can take several days, delay treatment, and subject parents and children to the anxiety that's associated with an undiagnosed, concerning illness. The new diagnostic test used an existing invention, a Grating-Coupled Fluorescence Plasmonic (GCFP) chip. It's a small gold chip that can analyze up to 400 biomarkers at once, whereas common methods are only able to analyze one biomarker at a time. The hope was to identify biomarkers that are only present, or are present in higher or lower numbers, when someone has MIS-C. The ability to include multiple biomarkers on one chip was very powerful, as several biomarkers were likely involved with this disease. The ability to identify these biomarkers would enable doctors to place a drop of a patient's saliva or blood on the chip, and the chip will quickly tell them if they have MIS-C or something else that looks like MIS-C. Participants were recruited at four hospitals in the U.S. and Colombia, collecting their blood, saliva, and health information, and followed them for up to four years. Scientists at five laboratories studied the blood and saliva to identify biomarkers, and also studied the microbiome to better understand why some children develop MIS-C or become seriously ill, and others do not.
Updated Date: 01/18/2024
Principal Investigator: Salazar, Juan C
Has Data Files: Yes
Study Domain: Multisystem Inflammatory Syndrome (MIS); Multisystem Inflammatory Syndrome in Children (MIS-C)
Data Collection Method: Molecular (Nucleic Acid/PCR) Testing Device
Keywords: Immunology; Pediatrics; Microbiome
Study Design: Case-Control
Multi-Center Study: TRUE
Study Sites: The Jackson Laboratory; Health Research; Inc./NYS Department of Health; University of Connecticut; Centro de Estudios en Infectología Pediátrica; NYU Grossman School of Medicine
Data Types: Genomic; Electronic Medical Records; Clinical; Questionnaires/Surveys
Study Start Date: 01/01/2021
Study End Date: 11/30/2022
Species: Human Data
Estimated Cohort Size: 660
Study Population Focus: Children
Publication URL: https://pubmed.ncbi.nlm.nih.gov/37064248/; https://pubmed.ncbi.nlm.nih.gov/34382611/; https://pubmed.ncbi.nlm.nih.gov/34528661/; https://pubmed.ncbi.nlm.nih.gov/35963365/
Acknowledgement Statement: This study was supported through funding, 3R61HD105613-02S1, for the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) as part of the RADx-rad program. The study was conducted in collaboration with Connecticut Children's Medical Center, the University of Connecticut, Centro de Estudios en Infectología Pediátrica, the Jackson Laboratories, the New York Department of Health, and New York University. Approved users should acknowledge the provision of data access by dbGaP for accession phs002609.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.
Funding Opportunity Announcement (FOA) Number: PA-20-272
NIH Grant or Contract Number(s): 3R61HD105613-02S1
Consent/Data Use Limitations: General Research Use
File Name | File Type | File Format(s) | # of Records | # of Variables | Metadata | Dictionary |
---|---|---|---|---|---|---|
Rad_023_613-01_2022_4_4_DATA_origcopy.csv | Tabular Data - Non-harmonized | csv | 561 | |||
Rad_023_613-01_2022_DATA_transformcopy.csv | Tabular Data - Harmonized | csv | 561 |