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
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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.
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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
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- Epidemiology and Global Health
- Genetics and Genomics
Background:
Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations.
Methods:
We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504).
Results:
Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p<5 × 10-5). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10-33) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10-9) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (–0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (–0.043±0.013 kg, p=0.0011) in the combined cohorts.
Conclusions:
This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.
Funding:
This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
Few national-level studies have evaluated the impact of ‘hybrid’ immunity (vaccination coupled with recovery from infection) from the Omicron variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Methods:
From May 2020 to December 2022, we conducted serial assessments (each of ~4000–9000 adults) examining SARS-CoV-2 antibodies within a mostly representative Canadian cohort drawn from a national online polling platform. Adults, most of whom were vaccinated, reported viral test-confirmed infections and mailed self-collected dried blood spots (DBSs) to a central lab. Samples underwent highly sensitive and specific antibody assays to spike and nucleocapsid protein antigens, the latter triggered only by infection. We estimated cumulative SARS-CoV-2 incidence prior to the Omicron period and during the BA.1/1.1 and BA.2/5 waves. We assessed changes in antibody levels and in age-specific active immunity levels.
Results:
Spike levels were higher in infected than in uninfected adults, regardless of vaccination doses. Among adults vaccinated at least thrice and infected more than 6 months earlier, spike levels fell notably and continuously for the 9-month post-vaccination. In contrast, among adults infected within 6 months, spike levels declined gradually. Declines were similar by sex, age group, and ethnicity. Recent vaccination attenuated declines in spike levels from older infections. In a convenience sample, spike antibody and cellular responses were correlated. Near the end of 2022, about 35% of adults above age 60 had their last vaccine dose more than 6 months ago, and about 25% remained uninfected. The cumulative incidence of SARS-CoV-2 infection rose from 13% (95% confidence interval 11–14%) before omicron to 78% (76–80%) by December 2022, equating to 25 million infected adults cumulatively. However, the coronavirus disease 2019 (COVID-19) weekly death rate during the BA.2/5 waves was less than half of that during the BA.1/1.1 wave, implying a protective role for hybrid immunity.
Conclusions:
Strategies to maintain population-level hybrid immunity require up-to-date vaccination coverage, including among those recovering from infection. Population-based, self-collected DBSs are a practicable biological surveillance platform.
Funding:
Funding was provided by the COVID-19 Immunity Task Force, Canadian Institutes of Health Research, Pfizer Global Medical Grants, and St. Michael’s Hospital Foundation. PJ and ACG are funded by the Canada Research Chairs Program.
