Clinical predictors for etiology of acute diarrhea in children in resource-limited settings.
AuthorBrintz, Ben J
Howard, Joel I
Platts-Mills, James A
Levine, Adam C
Nelson, Eric J
Pavia, Andrew T
Kotloff, Karen L
Leung, Daniel T
JournalPLoS Neglected Tropical Diseases
PublisherPublic Library of Science
MetadataShow full item record
AbstractBackground 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.
SponsorsThis work was supported by the National Institute of Health [R01 AI135114 to D.T.L., R01 AI125642 and R34 AI136783.
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/14062
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