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Geographical validation of the Smart Triage Model by age group

Author

Listed:
  • 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
  • on behalf of the Pediatric Sepsis CoLab

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 (

Suggested Citation

  • 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 & Sa, 2024. "Geographical validation of the Smart Triage Model by age group," PLOS Digital Health, Public Library of Science, vol. 3(7), pages 1-18, July.
    • Handle: RePEc:plo:pdig00:0000311
    DOI: 10.1371/journal.pdig.0000311
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    References listed on IDEAS

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    1. Arthur Mpimbaza & David Sears & Asadu Sserwanga & Ruth Kigozi & Denis Rubahika & Adam Nadler & Adoke Yeka & Grant Dorsey, 2015. "Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-13, July.
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