A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

Elife. 2021 Feb 2;10:e63009. doi: 10.7554/eLife.63009.


Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test' epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

Keywords: antibiotic stewardship; clinical decision support; clinical prediction rule; diarrhea; enteric infection; epidemiology; global health; virus.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Bacterial Agents / therapeutic use
  • Antimicrobial Stewardship
  • Child
  • Communicable Diseases / diagnosis
  • Decision Support Systems, Clinical*
  • Decision Support Techniques
  • Diagnostic Tests, Routine
  • Diarrhea / diagnosis*
  • Diarrhea / etiology*
  • Diarrhea / virology
  • Humans


  • Anti-Bacterial Agents