Geographical validation of the Smart Triage Model by age group

Cherri Zhang, Matthew O Wiens, Dustin Dunsmuir, Yashodani Pillay, Charly Huxford, David Kimutai, Emmanuel Tenywa, Mary Ouma, Joyce Kigo, Stephen Kamau, Mary Chege, Nathan Kenya-Mugisha, Savio Mwaka, Guy A Dumont, Niranjan Kissoon, Samuel Akech, J Mark Ansermino and on behalf of the Pediatric Sepsis CoLab

PLOS Digital Health, 2024, vol. 3, issue 7, 1-18

Abstract: Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000311

DOI: 10.1371/journal.pdig.0000311

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