Journal
JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE
Volume 7, Issue 1, Pages -Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/cts.2023.549
Keywords
Pediatrics; COVID-19; community challenges; machine learning; evaluation
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COVID-19 continues to pose a burden on the pediatric population due to various factors. There is a need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, there is currently no nationwide capability for developing validated computational tools to identify these patients using real-world data.
Introduction:With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist. Methods:HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions? Results:This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected. Conclusion:This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.
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