Project Details

Early Career

Status: Funded - Open

Establishing clinical predictors for improved management of pediatric diarrhea in the developing world

Joel Howard, MD


BACKGROUND: Diarrheal illness is a leading cause of morbidity and mortality among children worldwide, particularily in low and middle income countries (LMICs). Shigella infection is an important infection in this population and is treatable with antibiotics. As delayed treatment of this pathogen is associatd with growth failure, accurate and cost-effective determination of diarrhea etiology is important for proper case management in children and for public health.

GAP: The development of molecular diagnostic assays to detect diarrheal pathogens in stool has increased the speed and accuracy of pathogen detection, but at significant costs. Cost constraints in LMICs limit etiological diagnosis, and up to 70% of patients with acute diarrhea are prescribed empiric antibiotics, unnecessarily subjecting many patients to potential side effects.

HYPOTHESIS: We hypothesize conventions classically thought to be predictive of bacterial pathogens will underperform, such as bloody diarrhea, while unexpected clinical elements will be more suggestive of Shigella than previously recognized.

METHODS: We will use a combination of traditional and machine learning statistical methods to evaluate for predictors of Shigellosis contained within clinical elements of the GEMS (Global Enteric Multicenter Study) dataset, a large multicenter, international cohort of 15,283 children with diarrhea presenting for care.

RESULTS: Pending.

IMPACT: With improved understanding of pretest probabilities for Shigellosis, among other important pathogens, expensive testing can be limited to those who stand to benefit from treatment. This will enhance diagnostic stewardship in settings with already limited resources, while simultaneously improving patient outcomes by limiting unnecessary antibiotic use.

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