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Bergquist, Timothy; Wax, Marie; Bennett, Tellen D.; Moffitt, Richard A.; Gao, Jifan; Chen, Guanhua; Telenti, Amalio; Maher, M. Cyrus; Bartha, Istvan; Walker, Lorne; Orwoll, Benjamin E.; Mishra, Meenakshi; Alamgir, Joy; Cragin, Bruce L.; Ferguson, Christopher H.; Wong, Hui-Hsing; Deslattes Mays, Anne; Misquitta, Leonie; DeMarco, Kerry A.; Sciarretta, Kimberly L.; Patel, Sandeep A.
Journal of clinical and translational science, 01/2023, Letnik: 7, Številka: 1Journal Article
Abstract 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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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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