• Charly Huxford, Alireza Rafiei, Vuong Nguyen, Matthew O Wiens, J Mark Ansermino, Niranjan Kissoon, Elias Kumbakumba, Stephen Businge, Clare Komugisha, Mellon Tayebwa, Jerome Kabakyenga, Nathan Kenya Mugisha, Rishikesan Kamaleswaran, and Pediatric Sepsis Data CoLaboratory.
• Institute for Global Health, BC Children's Hospital and BC Women's Hospital + Health Centre, Vancouver, BC, Canada.
• Pediatr Crit Care Me. 2024 Nov 1; 25 (11): 104710501047-1050.

AbstractThe 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.

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