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A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.
Published on: November 7, 2018
Clinical Prediction Rule to Guide Diagnostic Testing for Shigellosis and Improve Antibiotic Stewardship for Pediatric
Sharia M Ahmed1, Ben J Brintz2, Patricia B Pavlinac3
1Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Insights
A new clinical prediction rule (CPR) accurately identifies Shigella diarrhea in children. This tool optimizes point-of-care diagnostic use, improving treatment and reducing unnecessary testing in low-resource settings.
Area of Science:
- Pediatric infectious diseases
- Clinical epidemiology
- Diagnostic test development
Background:
- Diarrheal diseases are a major cause of mortality in children under five.
- Accurate diagnosis of diarrhea etiology is crucial for targeted therapy but often limited by diagnostic availability in resource-poor settings.
- Shigella is a significant cause of childhood diarrhea.
Purpose of the Study:
- To develop a clinical prediction rule (CPR) to guide the use of point-of-care (POC) diagnostics for Shigella in children with acute diarrhea.
- To improve the identification of Shigella etiology in pediatric diarrhea cases.
Main Methods:
- Utilized clinical and demographic data from the Global Enteric Multicenter Study (GEMS) for model development.
- Employed random forests for variable screening and random forest/logistic regression with cross-validation for predictive performance assessment.
- Externally validated the CPR using data from the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study.
Main Results:
- Analyzed 5011 cases, with 1332 (27%) having Shigella etiology.
- The developed CPR demonstrated high predictive ability (AUC = 0.80) using age and caregiver-reported bloody diarrhea as key predictors.
- Implementing the CPR could identify three times more Shigella cases compared to current guidelines, with only 27% of cases requiring POC diagnostic testing.
Conclusions:
- A CPR can effectively guide the utilization of POC diagnostic tests for managing pediatric diarrhea.
- Optimizing diagnostic capacity through CPR implementation can lead to improved appropriate antibiotic use for Shigella infections.
Background:
Diarrheal diseases are a leading cause of death for children aged <5 years. Identification of etiology helps guide pathogen-specific therapy, but availability of diagnostic testing is often limited in low-resource settings. Our goal is to develop a clinical prediction rule (CPR) to guide clinicians in identifying when to use a point-of-care (POC) diagnostic for Shigella in children presenting with acute diarrhea.
Methods:
We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models for diarrhea of Shigella etiology in children aged ≤59 months presenting with moderate to severe diarrhea in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to externally validate our GEMS-derived CPR.
Results:
Of the 5011 cases analyzed, 1332 (27%) had diarrhea of Shigella etiology. Our CPR had high predictive ability (area under the receiver operating characteristic curve = 0.80 [95% confidence interval, .79-.81]) using the top 2 predictive variables, age and caregiver-reported bloody diarrhea. We show that by using our CPR to triage who receives diagnostic testing, 3 times more Shigella diarrhea cases would have been identified compared to current symptom-based guidelines, with only 27% of cases receiving a POC diagnostic test.
Conclusions:
We demonstrate how a CPR can be used to guide use of a POC diagnostic test for diarrhea management. Using our CPR, available diagnostic capacity can be optimized to improve appropriate antibiotic use.
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