dbGaP Study Accession: phs002569
NIH Institute/Center: NICHD
RADx Data Program: RADx-rad
Release Date: 11/07/2022
DOI: 10.60773/5ahc-nx20
Updated Date: 01/18/2024
Study Description: This study's objective was to understand which children are at highest risk for having severe consequences from SARS-CoV-2 infection. The study used clinical, epidemiologic, and sociodemographic data alongside specific biomarkers and created machine learning models designed to predict severe consequences. In order to accomplish this, a network of various networks and sites were established to examine subjects who have previously had infections or complications and subjects with active infections or complications, through both prospective enrollment and collection of data and biospecimens as well as through retrospective analyses of diverse datasets.
Principal Investigator: Kleinman, Lawrence C
Has Data Files: Yes
Study Domain: Artificial Intelligence and Machine Learning
Data Collection Method: Survey
Keywords: Severe SARS-CoV-2 Infection; Pediatric COVID; Risk Factors; Prospective Cohort; Longitudinal Data Analysis
Study Design: Case-Control
Multi-Center Study: TRUE
Study Sites: Bristol Myers Squibb Children’s Hospital @ Robert Wood Johnson University Hospital; Maria Fareri Children’s Hospital @ Westchester Medical Center; Yale Pediatric Genomics Discovery Program; DARTNet; Pediatric Rheumatology COVID Consortium (PRCC- Hackensack Meridian; NYU; Montefiore); Pediatric Research in Inpatient Settings (PRIS) Network
Data Types: Clinical; Family History; Electronic Medical Records; Questionnaires/Surveys; Individual Sequencing; Individual Phenotype; Individual Genotype; Immunological; Genomic
Study Start Date: 12/21/2020
Study End Date: 11/30/2022
Species: Human Data
Estimated Cohort Size: 120
Study Population Focus: Underserved/Vulnerable Population; Children
Publication URL: https://pubmed.ncbi.nlm.nih.gov/36751905/; https://pubmed.ncbi.nlm.nih.gov/35230433/; https://pubmed.ncbi.nlm.nih.gov/35073931/; https://pubmed.ncbi.nlm.nih.gov/34634317/; https://pubmed.ncbi.nlm.nih.gov/34387310/; https://pubmed.ncbi.nlm.nih.gov/37690792/; https://pubmed.ncbi.nlm.nih.gov/37798830/; https://pubmed.ncbi.nlm.nih.gov/37487417/
Acknowledgement Statement: This study was supported through funding, 4R61HD105619-02, for the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) as part of the RADx-rad program. Data were obtained through the COVID-19 Network of Networks Expanding Clinical and Translational approaches to Predict Severe Illness in Children (CONNECT to Predict SIck Children). Approved users should acknowledge the provision of data access by dbGaP for accession phs002569.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: RFA-OD-20-023
NIH Grant or Contract Number(s): 4R61HD105619-02
Consent/Data Use Limitations: General Research Use