Dataset from COVID-19 Network of Networks Expanding Clinical and Translational Approaches to Predict Severe Illness in Children (CONNECT to Predict SIck Children)

Background: This study aimed to understand which children were at highest risk for having severe consequences from SARS-CoV-2 infection.

Materials/Methods: The study used a two-phased approach that utilized a hybrid design that incorporated case control and cohort methods. Electronic data to identify predictors of severe SARS-CoV-2-related illness in cohorts of children was used, and then exploratory case control studies were developed to focus on discovering potential biomarkers and pertinent epidemiologic and clinical predictors for significant disease associated with SARS-CoV-2.

Outcome/Impact: Networks and sites were established to examine participants that previously had infections or complications, and participants with active infections or complications. The study used clinical, epidemiologic, and sociodemographic data alongside specific biomarkers and created machine learning models designed to predict severe consequences.