Clinical predictors for etiology of acute diarrhea in children in resource-limited settings

Clinical Impact of Multiplex Molecular Diagnostic Testing in Children With Acute Gastroenteritis Presenting to an Emergency Department: A Multicenter Prospective Study

BACKGROUND

Multiplex molecular diagnostic panels have greatly enhanced detection of gastrointestinal pathogens. However, data on the impact of these tests on clinical and patient-centered outcomes are limited.

METHODS

We conducted a prospective, multicenter, stepped-wedge trial to determine the impact of multiplex molecular testing at 5 academic children's hospitals on children presenting to the emergency department with acute gastroenteritis. Caregivers were interviewed on enrollment and 7-10 days after enrollment to determine symptoms, risk factors, subsequent medical visits, and impact on family members. During the pre-intervention period, diagnostic testing was performed at the clinician's discretion . During the intervention period, multiplex molecular testing was performed on all children, with results available to clinicians. The primary outcome was return visits to a healthcare provider within 10 days of enrollment.

RESULTS

Potential pathogens were identified by clinician-ordered tests in 19 of 571 (3.3%) in the pre-intervention period compared with 434 of 586 (74%) in the intervention period; clinically relevant pathogens were detected in 2.1% and 15%, respectively. In the multivariate model, the intervention was associated with a 21% reduction in the odds of any return visit (odds ratio, 0.79; 95% confidence interval, .70-.90) after adjusting for potential confounders. Appropriate treatment was prescribed in 11.3% compared with 19.6% during the intervention period (P = .22).

CONCLUSIONS

Routine molecular multiplex testing for all children who presented to the ED with acute gastroenteritis detected more clinically relevant pathogens and led to a 21% decrease in return visits. Additional research is needed to define patients most likely to benefit from testing. Clinical Trials Registration. NCT02248285.

Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review

Introduction

Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations.

Methods

We conducted a systematic review via a PubMed search for the period 1990-2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications.

Results

Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research.

Conclusions

Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.

Development of a prediction model for the acquisition of extended spectrum beta-lactam-resistant organisms in U.S. international travellers

BACKGROUND

Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies.

METHODS

We used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition.

RESULTS

A CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors.

CONCLUSIONS

We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

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.

National Hospital-Based Sentinel Surveillance for Cholera in Bangladesh: Epidemiological Results from 2014 to 2021

Despite focusing on cholera burden, epidemiologic studies in Bangladesh tend to be limited in geographic scope. National-level cholera surveillance data can help inform cholera control strategies and assess the effectiveness of preventive measures. Hospital-based sentinel surveillance among patients with suspected diarrhea in different sites across Bangladesh has been conducted since 2014. We selected an age-stratified sample of 20 suspected cholera cases each week from each sentinel site, tested stool for the presence of Vibrio cholerae O1/O139 by culture, and characterized antibiotic susceptibility in a subset of culture-positive isolates. We estimated the odds of being culture positive among suspected cholera cases according to different potential risk factors. From May 4, 2014 through November 30, 2021, we enrolled 51,414 suspected cases from our sentinel surveillance sites. We confirmed V. cholerae O1 in 5.2% of suspected cases through microbiological culture. The highest proportion of confirmed cholera cases was from Chittagong (9.7%) and the lowest was from Rangpur Division (0.9%). Age, number of purges, duration of diarrhea, occupation, and season were the most relevant factors in distinguishing cholera-positive suspected cases from cholera-negative suspected cases. Nationwide surveillance data show that cholera is circulating in Bangladesh and the southern region is more affected than the northern region. Antimicrobial resistance patterns indicate that multidrug resistance (resistance to three or more classes of antibiotics) of V. cholerae O1 could be a major threat in the future. Alignment of these results with Bangladesh's cholera-control program will be the foundation for future research into the efficacy of cholera-control initiatives.

Clinical Impact of Multiplex Molecular Diagnostic Testing in Children with Acute Gastroenteritis Presenting to An Emergency Department: A Multicenter Prospective Study

Background

Multiplex molecular diagnostic panels have greatly enhanced detection of gastrointestinal pathogens. However, data on the impact of these tests on clinical and patient-centered outcomes are limited.

Methods

We conducted a prospective, multicenter, stepped-wedge trial to determine the impact of multiplex molecular testing at five academic children's hospitals in children presenting to the ED with acute gastroenteritis. Caregivers were interviewed on enrollment and again 7-10 days after enrollment to determine symptoms, risk factors, subsequent medical visits, and impact on family members. During the pre-intervention period, diagnostic testing was performed at the discretion of clinicians. During the intervention period, multiplex molecular testing was performed on all children with results available to clinicians. Primary outcome was return visits to a health care provider within 10 days of enrollment.

Results

Potential pathogens were identified by clinician ordered tests in 19/571 (3.3%) in the pre-intervention period compared to 434/586 (74%) in the intervention period; clinically relevant pathogens were detected in 2.1% and 15% respectively. In the multivariate model adjusting for potential confounders, the intervention was associated with a 21% reduction in the odds of any return visit (OR 0.79; 95% CI 0.70-0.90). Appropriate treatment was prescribed in 11.3% compared to 19.6% during the intervention period(P=0.22).

Conclusions

Routine molecular multiplex testing for all children presenting to the ED with AGE detected more clinically relevant pathogens and led to a 21% decrease in return visits. Additional research is needed to define patients most likely to benefit from testing.

Derivation and External Validation of a Clinical Prediction Model for Viral Diarrhea Etiology in Bangladesh

Background

Antibiotics are commonly overused for diarrheal illness in many low- and middle-income countries, partly due to a lack of diagnostics to identify viral cases, in which antibiotics are not beneficial. This study aimed to develop clinical prediction models to predict risk of viral-only diarrhea across all ages, using routinely collected demographic and clinical variables.

Methods

We used a derivation dataset from 10 hospitals across Bangladesh and a separate validation dataset from the icddr,b Dhaka Hospital. The primary outcome was viral-only etiology determined by stool quantitative polymerase chain reaction. Multivariable logistic regression models were fit and externally validated; discrimination was quantified using area under the receiver operating characteristic curve (AUC) and calibration assessed using calibration plots.

Results

Viral-only diarrhea was common in all age groups (<1 year, 41.4%; 18-55 years, 17.7%). A forward stepwise model had AUC of 0.82 (95% confidence interval [CI], .80-.84) while a simplified model with age, abdominal pain, and bloody stool had AUC of 0.81 (95% CI, .78-.82). In external validation, the models performed adequately although less robustly (AUC, 0.72 [95% CI, .70-.74]).

Conclusions

Prediction models consisting of 3 routinely collected variables can accurately predict viral-only diarrhea in patients of all ages in Bangladesh and may help support efforts to reduce inappropriate antibiotic use.

Yield of Emergency Department Stool Culture Tests Among Children With Acute Gastroenteritis in Israel

Previous studies have attempted to predict a positive stool culture in pediatric patients with acute gastroenteritis (AGE), but most of them are either from developing countries or are outdated. In all, 276 patients with AGE and 560 control patients were analyzed for differences in clinical factors including the presence of fever, highest recorded temperature, bloody diarrhea, number of bowel movements in 24 hours prior to presentation, and the presence of seizures, as well as laboratory parameters including leukocyte count and C-reactive protein (CRP). Positive stool sample rate was 13.7%. The most common bacterial pathogen was Campylobacter jejuni. Bacterial AGE was significantly associated with fever >37.9°C, bloody diarrhea, higher stool passing frequency, seizures, and CRP levels. For pediatric patients who present to the emergency department with AGE and present without bloody diarrhea, fever, frequent stool passing, or seizures, a stool culture test is of poor yield and may not be necessary.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

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.

Weather variables as important clinical predictors of bacterial diarrhoea among international travellers

BACKGROUND

Clinicians and travellers often have limited tools to differentiate bacterial from non-bacterial causes of travellers' diarrhoea (TD). Development of a clinical prediction rule assessing the aetiology of TD may help identify episodes of bacterial diarrhoea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhoea among clinical, demographic and weather variables, as well as to develop and cross-validate a parsimonious predictive model.

METHODS

We collected de-identified clinical data from 457 international travellers with acute diarrhoea presenting to two healthcare centres in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal aetiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhoea.

RESULTS

We identified 195 cases of bacterial aetiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected aetiologies were average location-specific environmental temperature and red blood cell on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an area under the receiver operator curve of 0.73 using 3 variables with calibration intercept -0.01 (standard deviation, SD 0.31) and slope 0.95 (SD 0.36).

CONCLUSIONS

We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of aetiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.

Bacterial diarrhoea

PURPOSE OF REVIEW

This review describes recent findings about the burden of bacterial diarrhoea and its potential complications, newer diagnostics, the emerging threat of multidrug resistance, and the promise of vaccines in development.

RECENT FINDINGS

Introduction of rotavirus vaccines in over 110 countries has changed the landscape of diarrheal pathogens. In upper middle and high-income countries, the incidence of rotavirus-specific and all-cause gastroenteritis has declined substantially, and norovirus has become the major pathogen in many settings. Bacterial pathogens cause approximately 10-15% of episodes, most often Shigella, nontyphoidal Salmonella (NTS) Campylobacter and Shiga toxin-producing Escherichia coli (STEC). In lower income countries, bacterial pathogens remain a major cause of medically attended diarrhoea with Shigella, Campylobacter and enterotoxigenic Escherichia coli (ETEC) predominating. Multidrug-resistant strains of Shigella, NTS and, Campylobacter have emerged globally requiring judicious use of antibiotics according to current guidance.

SUMMARY

Management of bacterial diarrhoea includes standard fluid and electrolyte therapy, vigilance for potential complications, and use of antibiotics for children who have moderate-severe illness due to pathogens for which efficacy has been demonstrated, or for those at high risk for severe disease. The threat of multiply resistant strains provides impetus for preventive strategies such as development of vaccines.

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.

Etiology and Incidence of Moderate-to-Severe Diarrhea in Young Children in Niger

BACKGROUND

High-resolution data on the etiology of childhood diarrhea in countries with the highest burden and mortality remain sparse and are needed to inform burden estimates and prioritize interventions.

METHODS

We tested stool specimens collected between October 2014 and December 2017 from children under 2 years of age from the per-protocol population of a placebo-controlled clinical trial of a bovine rotavirus pentavalent vaccine (Rotasiil) in Niger. We tested 1729 episodes of moderate-to-severe diarrhea (Vesikari score ≥ 7) using quantitative PCR and estimated pathogen-specific burdens by age, season, severity, and trial intervention arm.

RESULTS

The 4 pathogens with the highest attributable incidence of diarrhea were Shigella (7.2 attributable episodes per 100 child-years; 95% confidence interval: 5.2, 9.7), Cryptosporidium (6.5; 5.8, 7.2), rotavirus (6.4; 5.9, 6.7), and heat-stabile toxin-producing enterotoxigenic Escherichia coli (ST-ETEC) (6.2; 3.1, 7.7). Cryptosporidium was the leading etiology of severe diarrhea (Vesikari score ≥ 11) and diarrhea requiring hospitalization. Shigella was the leading etiology of diarrhea in children 12-23 months of age but also had a substantial burden in the first year of life, with 60.5% of episodes of severe shigellosis occurring in infants. Shigella, Cryptosporidium, and ST-ETEC incidence peaked during the warmer and wetter period and coincided with peak all-cause diarrhea incidence.

CONCLUSIONS

In this high-burden setting, the leading diarrheal pathogens were Shigella, Cryptosporidium, rotavirus, and ST-ETEC, and each was disproportionately seen in infants. Vaccine development should target these pathogens, and the impact of vaccine schedule on diarrhea burden in the youngest children will need to be considered.

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

Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.