The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda
- PMID: 38904442
- PMCID: PMC11534513
- DOI: 10.1097/PCC.0000000000003556
The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda
Abstract
The aim of this "Technical Note" is to inform the pediatric critical care data research community about the "2024 Pediatric Sepsis Data Challenge." This competition aims to facilitate the development of open-source algorithms to predict in-hospital mortality in Ugandan children with sepsis. The challenge is to first develop an algorithm using a synthetic training dataset, which will then be scored according to standard diagnostic testing criteria, and then be evaluated against a nonsynthetic test dataset. The datasets originate from admissions to six hospitals in Uganda (2017-2020) and include 3837 children, 6 to 60 months old, who were confirmed or suspected to have a diagnosis of sepsis. The synthetic dataset was created from a random subset of the original data. The test validation dataset closely resembles the synthetic dataset. The challenge should generate an optimal model for predicting in-hospital mortality. Following external validation, this model could be used to improve the outcomes for children with proven or suspected sepsis in low- and middle-income settings.
Copyright © 2024 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.
Conflict of interest statement
Ms. Huxford’s institution received funding from Grand Challenges Canada, Thrasher Research Fund, BC Children’s Hospital Foundation, and Mining4Life. Dr. Businge received funding from the Pediatric Sepsis Data CoLaboratory, the World Federation of Pediatric Intensive and Critical Care Societies, the University of British Columbia, and the BC Children’s Hospital Foundation. Dr. Komugisha received support for article research from Grand Challenges Canada. Dr. Tayebwa received funding from the Mbarara University of Science and Technology. Dr. Kamaleswaran received support for article research from the National Institutes of Health. The remaining authors have disclosed that they do not have any potential conflicts of interest.
References
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- Heneghan JA, Walker SB, Fawcett A, et al. : The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: use of supervised machine learning applications in pediatric critical care medicine research. Pediatr Crit Care Med 2023. Dec 7 [online ahead of print doi: 10.1097/PCC.0000000000003425]. - DOI - PMC - PubMed
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