External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: A multicenter study in Bangladesh and Mali

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 a previously derived and internally validated model consisting of patient-specific ('present patient') clinical variables (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) as well as location-specific viral diarrhea seasonality curves. The performance of additional models using the 'present patient' data combined with other external data sources including location-specific climate, data, recent patient data, and historical population-based prevalence were also evaluated in secondary analysis. 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 present patient+ viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large alpha = -0.393 (-0.455--0.331) and calibration slope beta = 1.287 (1.207-1.367). By site, the present patient+ recent patient model performed best in Mali with an AUC of 0.783 (0.705-0.86); the present patient+ viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595-0.825). Conclusions: 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. Funding: Funding for this study was provided through grants from the Bill and Melinda GatesFoundation (OPP1198876) and the National Institute of Allergy and Infectious Diseases (R01AI135114). Several investigators were also partially supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116163). This investigation was also supported by the University of Utah Population Health Research (PHR) Foundation, with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002538. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in the study design, data collection, data analysis, interpretation of data, or in the writing or decision to submit the manuscript for publication. eLife digest Diarrhea is one of the most common illnesses among children worldwide. In low- and middle-income countries with limited health care resources, it can be deadly. Diarrhea can be caused by infections with viruses or bacteria. Antibiotics can treat bacterial infections, but they are not effective against viral infections. It can often be difficult to determine the cause of diarrhea. As a result, many clinicians just prescribe antibiotics. However, since diarrhea in young children is often due to viral infections, prescribing unnecessary antibiotics can cause children to have side effects without any benefit. Excessive use of antibiotics also contributes to the development of bacteria that are resistant to antibiotics, making infections harder to treat. Scientists are working to develop mobile health tools or 'apps' that may help clinicians identify the cause of diarrhea. Using computer algorithms to analyze information about the patient and seasonal infection patterns, the apps predict whether a bacterial or viral infection is the likely culprit. These tools may be particularly useful in low- or middle-income country settings, where clinicians have limited access to testing for bacteria or viruses. Garbern, Nelson et al. previously built an app to help distinguish cases of viral diarrhea in children in Mali and Bangladesh. Now, the researchers have put their app to the test in the real-world in a new group of patients to verify it works. In the experiments, nurses in Mali and Bangladesh used the app to predict whether a child with diarrhea had a viral or non-viral infection. The children's stool was then tested for viral or bacterial DNA to confirm whether the prediction was correct. The experiments showed the app accurately identified viral cases of diarrhea. The experiments also showed that customizing the app to local conditions may further improve its accuracy. For example, a version of the app that factored in seasonal virus transmission performed the best in Bangladesh, while a version that factored in data from recent patients in the past few weeks performed the best in Mali. Garbern and Nelson et al. are now testing whether their app could help reduce unnecessary use of antibiotics in children with diarrhea. If it does, it may help minimize antibiotic resistance and ensure more children get appropriate diarrhea care.

Automated summary

This study developed a mobile software application that accurately predicts the etiology of acute diarrhea in children. The app was validated in Bangladesh and Mali and showed promising results in identifying viral-only diarrhea. This app has the potential to reduce inappropriate antibiotic use for diarrhea treatment in low- and middle-income countries.