External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: a multicenter study in Bangladesh and Mali
- Brown University, United States
- University of Florida, United States
- International Centre for Diarrhoeal Disease Research, Bangladesh
- Centre for Vaccine Development, Mali
- University of Utah, United States
- Rhode Island Hospital, United States
- Center for Vaccine Development, Mali
- University of Maryland School of Medicine, United States
- University of Kentucky, United States
- University of Virginia, United States
- University of Maryland, United States
Abstract
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 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.
Funding: Funding for this study was provided through grants from the Bill and Melinda Gates Foundation (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.
Data availability
The de-identified dataset is included as Supplementary File 5 and has been deposited on Dryad. The modeling code and additional files needed to run the code are deposited on GitHub at https://github.com/LeungLab/DiaPR_Phase1
-
Data from: Diarrhea Etiology Prediction Validation Dataset - Bangladesh and MaliDryad Digital Repository doi:10.5061/dryad.0rxwdbs19.
Article and author information
Author details
Stephanie Chow Garbern
Funding
Bill and Melinda Gates Foundation (OPP1198876)
- Daniel T Leung
National Institute of Allergy and Infectious Diseases (R01AI135114)
- Daniel T Leung
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116163)
- Monique Gainey
National Center for Advancing Translational Sciences (UL1TR002538)
- Ben J Brintz
National Institutes of Health (R21TW010182)
- Eric J Nelson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Informed consent was obtained from all study participants as described in Materials and Methods. Ethical approval was obtained from the icddr,b Ethical Review Committee (PR-19095), the University of Sciences, Techniques, and Technologies of Bamako Ethics Committee (2019-153), and the University of Utah Institutional Review Board (IRB_00121790).
Copyright
© 2022, Garbern et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 898
- views
-
- 150
- downloads
-
- 16
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: a multicenter study in Bangladesh and Mali
eLife 11:e72294.
https://doi.org/10.7554/eLife.72294
Further reading
-
- Epidemiology and Global Health
Artificially sweetened beverages containing noncaloric monosaccharides were suggested as healthier alternatives to sugar-sweetened beverages. Nevertheless, the potential detrimental effects of these noncaloric monosaccharides on blood vessel function remain inadequately understood. We have established a zebrafish model that exhibits significant excessive angiogenesis induced by high glucose, resembling the hyperangiogenic characteristics observed in proliferative diabetic retinopathy (PDR). Utilizing this model, we observed that glucose and noncaloric monosaccharides could induce excessive formation of blood vessels, especially intersegmental vessels (ISVs). The excessively branched vessels were observed to be formed by ectopic activation of quiescent endothelial cells (ECs) into tip cells. Single-cell transcriptomic sequencing analysis of the ECs in the embryos exposed to high glucose revealed an augmented ratio of capillary ECs, proliferating ECs, and a series of upregulated proangiogenic genes. Further analysis and experiments validated that reduced foxo1a mediated the excessive angiogenesis induced by monosaccharides via upregulating the expression of marcksl1a. This study has provided new evidence showing the negative effects of noncaloric monosaccharides on the vascular system and the underlying mechanisms.
-
- Epidemiology and Global Health
- Microbiology and Infectious Disease
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.
