dbGaP Study Accession: phs002569

NIH Institute/Center: NICHD

RADx Data Program: RADx-rad

Release Date: 11/07/2022

DOI: 10.60773/5ahc-nx20

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.

Updated Date: 01/18/2024

Principal Investigator: Kleinman, Lawrence C

Has Data Files: Yes

Study Domain: Artificial Intelligence or Machine Learning

Data Collection Method: Survey

Keywords: Longitudinal Data Analysis; Pediatric COVID; Prospective Cohort; Risk Factors; Severe SARS-CoV-2 Infection

Study Design: Case-Control

Multi-Center Study: Yes

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; Electronic Medical Records; Family History; Genomic; Immunological; Individual Genotype; Individual Phenotype; Individual Sequencing; Questionnaire or Survey

Study Start Date: 12/21/2020

Study End Date: 11/30/2022

Species: Human

Estimated Cohort Size: 120

Study Population Focus: Children; Underserved or Vulnerable Populations

Publication URL: https://pubmed.ncbi.nlm.nih.gov/34387310/; https://pubmed.ncbi.nlm.nih.gov/34634317/; https://pubmed.ncbi.nlm.nih.gov/35073931/; https://pubmed.ncbi.nlm.nih.gov/35230433/; https://pubmed.ncbi.nlm.nih.gov/36751905/; https://pubmed.ncbi.nlm.nih.gov/37487417/; https://pubmed.ncbi.nlm.nih.gov/37690792/; https://pubmed.ncbi.nlm.nih.gov/37798830/

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