• Clinical predictors for etiology of acute diarrhea in children in resource-limited settings.

      Brintz, Ben J; Howard, Joel I; Haaland, Benjamin; Platts-Mills, James A; Greene, Tom; Levine, Adam C; Nelson, Eric J; Pavia, Andrew T; Kotloff, Karen L; Leung, Daniel T (Public Library of Science, 2020-10-09)
      Background Diarrhea is one of the leading causes of childhood morbidity and mortality in lower-and mid-dle-income countries. In such settings, access to laboratory diagnostics are often limited, and decisions for use of antimicrobials often empiric. Clinical predictors are a potential non-laboratory method to more accurately assess diarrheal etiology, the knowledge of which could improve management of pediatric diarrhea. Methods We used clinical and quantitative molecular etiologic data from the Global Enteric Multicen-ter Study (GEMS), a prospective, case-control study, to develop predictive models for the etiology of diarrhea. Using random forests, we screened the available variables and then assessed the performance of predictions from random forest regression models and logistic regression models using 5-fold cross-validation. Results We identified 1049 cases where a virus was the only etiology, and developed predictive models against 2317 cases where the etiology was known but non-viral (bacterial, proto-zoal, or mixed). Variables predictive of a viral etiology included lower age, a dry and cold season, increased height-for-age z-score (HAZ), lack of bloody diarrhea, and presence of vomiting. Cross-validation suggests an AUC of 0.825 can be achieved with a parsimonious model of 5 variables, achieving a specificity of 0.85, a sensitivity of 0.59, a NPV of 0.82 and a PPV of 0.64. Conclusion Predictors of the etiology of pediatric diarrhea can be used by providers in low-resource settings to inform clinical decision-making. The use of non-laboratory methods to diagnose viral causes of diarrhea could be a step towards reducing inappropriate antibiotic prescription worldwide.
    • A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea.

      Brintz, Ben J; Haaland, Benjamin; Howard, Joel; Chao, Dennis L; Proctor, Joshua L; Khan, Ashraful I; Ahmed, Sharia M; Keegan, Lindsay T; Greene, Tom; Keita, Adama Mamby; et al. (eLife Sciences Publications, 2021-02-02)
      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. © 2021, Brintz et al.