10.5061/DRYAD.0RXWDBS19
Leung, Daniel
University of Utah
Brintz, Ben
0000-0003-4695-0290
University of Utah
Garbern, Stephanie
Brown University
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
Dryad
dataset
2021
Diarrhea
diarrhoea
antibiotic stewardship
AMR
enteropathogens
enteric infections
Bangladesh
Mali
mobile health
smartphone
clinical prediction model
Clinical decision support
FOS: Medical and health sciences
Bill & Melinda Gates Foundation
https://ror.org/0456r8d26
OPP1198876
National Institute of Allergy and Infectious Diseases
https://ror.org/043z4tv69
R01AI135114
2021-09-07T00:00:00Z
2021-09-07T00:00:00Z
en
https://doi.org/10.7554/eLife.72294
https://doi.org/10.5281/zenodo.5487043
452250 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.