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dc.contributor.authorGarbern, Stephanie Chow
dc.contributor.authorNelson, Eric J
dc.contributor.authorNasrin, Sabiha
dc.contributor.authorKeita, Adama Mamby
dc.contributor.authorBrintz, Ben J
dc.contributor.authorGainey, Monique
dc.contributor.authorBadji, Henry
dc.contributor.authorNasrin, Dilruba
dc.contributor.authorHoward, Joel
dc.contributor.authorTaniuchi, Mami
dc.contributor.authorPlatts-Mills, James A
dc.contributor.authorKotloff, Karen L
dc.contributor.authorHaque, Rashidul
dc.contributor.authorLevine, Adam C
dc.contributor.authorSow, Samba O
dc.contributor.authorAlam, Nur Haque
dc.contributor.authorLeung, Daniel T
dc.date.accessioned2022-02-11T15:26:44Z
dc.date.available2022-02-11T15:26:44Z
dc.date.issued2022-02-09
dc.identifier.urihttp://hdl.handle.net/10713/17956
dc.description.abstractBackground: 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 α=-0.393 (-0.455 - -0.331) and calibration slope β=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). 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.en_US
dc.description.urihttps://doi.org/10.7554/eLife.72294en_US
dc.language.isoenen_US
dc.publishereLife Sciences Publicationsen_US
dc.relation.ispartofeLifeen_US
dc.rights© 2022, Garbern et al.en_US
dc.subjectepidemiologyen_US
dc.subjectglobal healthen_US
dc.subjecthumanen_US
dc.subjectmedicineen_US
dc.titleExternal validation of a mobile clinical decision support system for diarrhea etiology prediction in children: a multicenter study in Bangladesh and Mali.en_US
dc.typeArticleen_US
dc.identifier.doi10.7554/eLife.72294
dc.identifier.pmid35137684
dc.source.journaltitleeLife
dc.source.volume11
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryEngland


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