Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.

Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application (“App”) for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of “patient-intrinsic” information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.

Results: Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 – -0.331) and calibration slope β=1.287 (1.207 – 1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).

Conclusion: The app accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the app’s potential use in diagnostic and antimicrobial stewardship are underway.

The dataset was collected in Bangladesh at the Dhaka Hospital of the International Center for Diarrhoeal Disease Research, Bangladesh (icddr,b) rehydration (short stay) unit and in Mali at the Centres de Santé de Référence (CSREF) and the Centres de Santé Communautaires (CSCOM) in Commune V and VI in Bamako, Mali. This dataset was used to produce a manuscript under review at eLife.

The readme file contains an explanation of each of the variables in the dataset and measurement units when applicable.