Clinical prediction rule to guide diagnostic testing for Shigellosis and improve antibiotic stewardship for pediatric diarrhea

Background: Diarrheal diseases are a leading cause of death for children under-5. Identification of etiology helps guide pathogen-specific therapy, but availability of diagnostic testing is often limited in low resource settings. Our goal is to develop a clinical prediction rule (CPR) to guide clinicians in identifying when to use a point-of-care diagnostic for Shigella in children presenting with acute diarrhea. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models for diarrhea of Shigella etiology in children [≤]59 months presenting with moderate-to-severe diarrhea in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to externally validate our GEMS-derived CPR. Results: Of the 5011 cases analyzed, 1332 (27%) had diarrhea of Shigella etiology. Our CPR had high predictive ability (AUC=0.80 (95% CI: 0.79, 0.81) using the top two predictive variables, age and caregiver reported bloody diarrhea. We show that by using our CPR to triage who receives diagnostic testing, 3 times more Shigella diarrhea cases would have been identified compared to current symptom-based guidelines, with only 27% of cases receiving a point-of-care diagnostic test. Conclusions: We demonstrate how a clinical prediction rule can be used to guide use of a point-of-care diagnostic test for diarrhea management. Using our CPR, available diagnostic capacity can be optimized to improve appropriate antibiotic use.