A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology

Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.

Azithromycin for Bacterial Watery Diarrhea: A Reanalysis of the AntiBiotics for Children With Severe Diarrhea (ABCD) Trial Incorporating Molecular Diagnostics

BACKGROUND

Bacterial pathogens cause substantial diarrhea morbidity and mortality among children living in endemic settings, yet antimicrobial treatment is only recommended for dysentery or suspected cholera.

METHODS

AntiBiotics for Children with severe Diarrhea was a 7-country, placebo-controlled, double-blind efficacy trial of azithromycin in children 2-23 months of age with watery diarrhea accompanied by dehydration or malnutrition. We tested fecal samples for enteric pathogens utilizing quantitative polymerase chain reaction to identify likely and possible bacterial etiologies and employed pathogen-specific cutoffs based on genomic target quantity in previous case-control diarrhea etiology studies to identify likely and possible bacterial etiologies.

RESULTS

Among 6692 children, the leading likely etiologies were rotavirus (21.1%), enterotoxigenic Escherichia coli encoding heat-stable toxin (13.3%), Shigella (12.6%), and Cryptosporidium (9.6%). More than one-quarter (1894 [28.3%]) had a likely and 1153 (17.3%) a possible bacterial etiology. Day 3 diarrhea was less common in those randomized to azithromycin versus placebo among children with a likely bacterial etiology (risk difference [RD]likely, -11.6 [95% confidence interval {CI}, -15.6 to -7.6]) and possible bacterial etiology (RDpossible, -8.7 [95% CI, -13.0 to -4.4]) but not in other children (RDunlikely, -0.3% [95% CI, -2.9% to 2.3%]). A similar association was observed for 90-day hospitalization or death (RDlikely, -3.1 [95% CI, -5.3 to -1.0]; RDpossible, -2.3 [95% CI, -4.5 to -.01]; RDunlikely, -0.6 [95% CI, -1.9 to .6]). The magnitude of risk differences was similar among specific likely bacterial etiologies, including Shigella.

CONCLUSIONS

Acute watery diarrhea confirmed or presumed to be of bacterial etiology may benefit from azithromycin treatment.

CLINICAL TRIALS REGISTRATION

NCT03130114.

Comprehensive diagnostic testing identifies diverse aetiologies of acute febrile illness among hospitalised children and adults in Sri Lanka: a prospective cohort study

Introduction

Acute febrile illness (AFI) is a common cause of hospital admissions in tropical settings. Identifying AFI aetiology is essential for guiding clinicians' diagnoses and developing diagnostic and management guidelines. We used rigorous, gold-standard testing for diverse viral and bacterial pathogens to confirm the aetiology of AFI in southern Sri Lanka.

Methods

We prospectively enrolled children and adults with AFI admitted to Teaching Hospital Karapitiya, Galle, the largest tertiary care hospital in Southern Province, Sri Lanka from June 2012 to May 2013. We obtained sociodemographic and clinical data, an acute blood sample, a nasopharyngeal sample, and a urine sample at enrolment and a convalescent blood sample 2-4 weeks later. Laboratory testing was conducted for dengue, respiratory viruses, leptospirosis, scrub typhus, spotted fever group (SFG) and typhus group (TG) rickettsioses and Q fever.

Results

A total of 976 patients were enrolled and a convalescent visit was completed in 878 (90.0%). Median age was 26.9 (14.2-41.4) years and the majority were male (628, 64.3%). A viral or bacterial aetiology was identified in 660 (67.6%). A viral aetiology was identified in 534 (54.7%), including 388 (39.8%) with dengue and 171 (17.5%) with respiratory viruses. Bacterial infection was found in 138 (14.1%) and included leptospirosis (79, 8.1%), SFG (17, 1.7%), TG (7, 0.7%), scrub typhus (53, 5.4%) and Q fever (5, 0.5%). Antibiotics were prescribed at enrolment for 45.5% with viral infections and 62.3% with bacterial infection. Overall, sensitivity of clinical diagnosis was low at approximately 50%.

Conclusion

We identified an aetiology of AFI in two-thirds of patients in a setting where malaria is non-endemic. Sensitivity of clinical diagnosis was low, with overuse of antibiotics for viral infections and underuse of antibiotics for bacterial infections. Diagnostic algorithms for AFI may help improve clinical management in this and comparable settings with diverse AFI aetiologies.

Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea

Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.

Enteric Pathogen Testing Importance for Children with Acute Gastroenteritis: a Modified Delphi Study

The application of clinical diagnostics for gastroenteritis in children has implications for a broad collection of stakeholders, impacting clinical care, communicable disease control, and laboratory utilization. To support diagnostic stewardship as gastroenteritis testing options continue to advance, it is critical to understand which enteropathogens constitute priorities for testing across stakeholder groups. Using a modified Delphi technique, we elicited opinions of subject matter experts to determine clinical and public health testing priorities. There was a high level of overall agreement (≥80%) among stakeholders (final round n = 15) that testing was important for Campylobacter, Escherichia coli O157 and other Shiga toxin-producing E. coli, Salmonella, Shigella, Vibrio, Yersinia, norovirus, and rotavirus. Immunocompromised children were identified as a special population that warranted the additional testing of three to four bacterial and parasitic targets. To support these clinical and public health testing priorities, diagnostic stewardship strategies can be employed, such as educating clinicians, developing new decision support tools, and using multiplex testing in concert with selective result reporting and annotation. IMPORTANCE Children with diarrhea and vomiting who seek care can be infected with a wide variety of infectious agents. This study reports findings from a survey of clinical, public health, and laboratory subject matter experts on the infectious agents that are most important to test for. The majority agreed on the importance of testing children likely infected with several bacterial agents, as well as two common viruses. Although confirming a child is positive for a viral agent is unlikely to change clinical care, participants noted the importance of monitoring these viruses for public health purposes. To avoid over-testing children, however, these results should be used to support diagnostic stewardship strategies and design new decision support tools.

Improving Antibiotic Stewardship for Diarrheal Disease With Probability-Based Electronic Clinical Decision Support: A Randomized Crossover Trial

Importance

Inappropriate use of antibiotics for diarrheal illness can result in adverse effects and increase in antimicrobial resistance.

Objective

To determine whether the diarrheal etiology prediction (DEP) algorithm, which uses patient-specific and location-specific features to estimate the probability that diarrhea etiology is exclusively viral, impacts antibiotic prescriptions in patients with acute diarrhea.

Design, Setting, and Participants

A randomized crossover study was conducted to evaluate the DEP incorporated into a smartphone-based electronic clinical decision-support (eCDS) tool. The DEP calculated the probability of viral etiology of diarrhea, based on dynamic patient-specific and location-specific features. Physicians were randomized in the first 4-week study period to the intervention arm (eCDS with the DEP) or control arm (eCDS without the DEP), followed by a 1-week washout period before a subsequent 4-week crossover period. The study was conducted at 3 sites in Bangladesh from November 17, 2021, to January 21, 2021, and at 4 sites in Mali from January 6, 2021, to March 5, 2021. Eligible physicians were those who treated children with diarrhea. Eligible patients were children between ages 2 and 59 months with acute diarrhea and household access to a cell phone for follow-up.

Interventions

Use of the eCDS with the DEP (intervention arm) vs use of the eCDS without the DEP (control arm).

Main Outcomes and Measures

The primary outcome was the proportion of children prescribed an antibiotic.

Results

A total of 30 physician participants and 941 patient participants (57.1% male; median [IQR] age, 12 [8-18] months) were enrolled. There was no evidence of a difference in the proportion of children prescribed antibiotics by physicians using the DEP (risk difference [RD], -4.2%; 95% CI, -10.7% to 1.0%). In a post hoc analysis that accounted for the predicted probability of a viral-only etiology, there was a statistically significant difference in risk of antibiotic prescription between the DEP and control arms (RD, -0.056; 95% CI, -0.128 to -0.01). No known adverse effects of the DEP were detected at 10-day postdischarge.

Conclusions and Relevance

Use of a tool that provides an estimate of etiological likelihood did not result in a significant change in overall antibiotic prescriptions. Post hoc analysis suggests that a higher predicted probability of viral etiology was linked to reductions in antibiotic use.

Trial Registration

Clinicaltrials.gov Identifier: NCT04602676.

External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: a multicenter study in Bangladesh and Mali

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.