dbGaP Study Accession: phs002585

NIH Institute/Center: NICHD

RADx Data Program: RADx-rad

DOI: 10.60773/q345-4e35

Release Date: 07/21/2022

Study Description: This work was directed at characterizing pediatric COVID-19 and stratifying incoming patients by projected (future) disease severity. Such stratification has several implications: immediately improving treatment planning, and as disease mechanistic pathways are uncovered, directing treatment. Predicting future severity informed the risks of outpatient treatment; to the patients themselves, their family, other caregivers/cohabitants, and to schools and employers. As varying levels of reopening are adopted across the country (and the world), such prognostication informed policy on the handling of pediatric carriers in the community. Based on preliminary analysis, it is asserted that a combination of novel assays including quantitative serology inflammatory markers (cytokine/chemokine profiles, immune profiles), transcriptomics, epigenomics, longitudinal physiological monitoring, time series analysis, imaging, radiomics and clinical observation including social determinants of health, contains adequate information even at early stages of infection to stratify the disease and predict disease severity. An artificial intelligence/machine learning approach was utilized to integrate this rich and heterogeneous dataset, characterize the spectrum of disease and identify biosignatures that predict severity in progressive disease. To facilitate translation of the approaches developed in this work to a wide user community, a Translational Development function was incorporated to oversee the design-control process and ensure readiness of the methods for regulatory review. Incorporated into the timelines are appropriate regulatory milestones intended to conform with the Emergency Use Authorization (EUA) programs in effect for SARS-CoV-2 diagnostics.

Updated Date: 01/18/2024

Principal Investigator: Annapragada, Ananth V

Has Data Files: Yes

Study Domain: Artificial Intelligence and Machine Learning; Social Determinants of Health

Data Collection Method: Real-World Data

Keywords: Data Science; Pediatric COVID-19; Timeseries Forecasting

Study Design: Longitudinal Cohort

Multi-Center Study: FALSE

Data Types: Electronic Medical Records

Study Start Date: 01/01/2021

Study End Date: 11/30/2022

Species: Human Data

Estimated Cohort Size: 7200

Study Population Focus: Children

Publication URL: https://pubmed.ncbi.nlm.nih.gov/35439401/; https://pubmed.ncbi.nlm.nih.gov/35568731/; https://pubmed.ncbi.nlm.nih.gov/36326636/; https://pubmed.ncbi.nlm.nih.gov/37685502/; https://pubmed.ncbi.nlm.nih.gov/34911654/; https://pubmed.ncbi.nlm.nih.gov/34560062/; https://pubmed.ncbi.nlm.nih.gov/34022420/; https://pubmed.ncbi.nlm.nih.gov/34977312/

Acknowledgement Statement: This study was supported through funding, 4R61HD105593-02, for the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) as part of the RADx-rad program. This data was collected and awarded to the Baylor College of Medicine, PI: Annapragada. Approved users should acknowledge the provision of data access by dbGaP for accession phs002585.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): 4R61HD105593-02

Consent/Data Use Limitations: General Research Use