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Introduction: Evaluation of potential outcomes of COVID-19-affected pneumonia patients using computed tomography scans may not be conceivable in low-resource settings. Thus, we aimed to evaluate the performance of chest X-ray scoring in predicting the disease severity and outcomes of adults hospitalised with COVID-19. Methods: This was a retrospective chart analysis consuming data from COVID-19-positive adults who had chest X-ray availability and were admitted to a temporary COVID unit, in Bangladesh from 23rd April 2020 to 15th November 2021. At least one clinical intensivist and one radiologist combinedly reviewed each admission chest X-ray for the different lung findings. Chest X-ray scoring varied from 0 to 8, depending on the area of lung involvement with 0 indicating no involvement and 8 indicating ⩾75% involvement of both lungs. The receiver operating characteristic curve was used to determine the optimum chest X-ray cut-off score for predicting the fatal outcomes. Result: A total of 218 (82.9%) out of 263 COVID-19-affected adults were included in the study. The receiver operating characteristic curve demonstrated the optimum cut-off as ⩾3 and ⩾5 for disease severity and death, respectively. In multivariate logistic regression analysis, a chest X-ray score of ⩾3 was found to be independently associated with disease severity (aOR: 8.70; 95% CI: 3.82, 19.58, p

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest.

Abstract Data are limited on the prevalence and outcome of anemia and its risk on mortality among children under five years of age hospitalized for pneumonia/severe pneumonia. Thus, we conducted a secondary analysis of data extracted from Dhaka Hospital of International Centre for Diarrhoeal Disease Research, Bangladesh to address the evidence gap. Among 3468 children fulfilling the study criteria,1712 (49.4%) had anemia. If children aged ≤ 1.0, > 1.0 to 2.0, > 2.0 to

Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320.

Despite the reduction of death from pneumonia over recent years, pneumonia has still been the leading infectious cause of death in under-five children for the last several decades. Unconsciousness is a critical condition in any child resulting from any illness. Once it occurs during a pneumonia episode, the outcome is perceived to be fatal. However, data on children under five with pneumonia having unconsciousness are scarce. We’ve retrospectively analyzed the data of under-five children admitted at the in-patient ward of Dhaka Hospital of icddr,b during 1 January 2014 and 31 December 2017 with World Health Organization classified pneumonia or severe pneumonia. Children presented with or without unconsciousness were considered as cases and controls respectively. Among a total of 3,876 children fulfilling the inclusion criteria, 325 and 3,551 were the cases and the controls respectively. A multivariable logistic regression analysis revealed older children (8 months vs. 7.9 months) (adjusted odds ratio, aOR 1.02, 95% CI: 1.004–1.04, p = 0.015), hypoxemia (aOR 3.22, 95% CI: 2.39–4.34, p

BackgroundPneumonia has been the leading infectious cause of morbidity and mortality in children under 5 years of age for the last several decades. Although most of these deaths occur due to respiratory failure, published data are limited regarding predicting factors and outcomes of respiratory failure in children hospitalized with pneumonia or severe pneumonia.ObjectiveThis study aimed to explore the prevalence, predicting factors, and outcomes of respiratory failure in children under-five with pneumonia or severe pneumonia.MethodsIn this retrospective chart analysis, we enrolled children under 5 years of age hospitalized with pneumonia or severe pneumonia in the Dhaka Hospital of International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) between August 2013 and December 2017. Comparisons were made between children with respiratory failure (n = 212) and those without respiratory failure (n = 4,412). Respiratory failure was defined when the oxygen saturation/fraction of inspired oxygen (SpO2/FiO2) was

Background As COVID-19 was declared a global pandemic, the major focus of healthcare organizations shifted towards preparing healthcare systems to handle the inevitable COVID-19 burden at different phases and levels. A series of in-person training programs were operated in collaboration with government and partner organizations for the healthcare workers (HCW) of Bangladesh. This study aimed to assess the knowledge of HCWs regarding SARS-CoV-2 infection, their case management, infection prevention and control to fight against the ongoing pandemic. Methods As a part of the National Preparedness and Response Plan for COVID-19 in Bangladesh, the training program was conducted at four district-level hospitals and one specialized hospital in Bangladesh from July 1, 2020 to June 30, 2021. A total of 755 HCWs participated in the training sessions. Among them, 357 (47%) were enrolled for the evaluation upon completion of the data, collected from one district hospital (Feni) and one specialized hospital (National Institute of Mental Health). Results The mean percentage of pre-test and post-test scores of all the participants were found to be 57% (95% CI 8.34–8.91; p 0.01) and 65% (95% CI 9.56–10.15; p Conclusion Considering the series of waves of COVID-19 pandemic with newer variants, the present paper underscores the importance of implementing the structured in-person training program on case management, infection prevention and control for the HCWs that may help for successful readiness prior to future pandemics that may further help to minimize the pandemic related fatal consequences.

Among all intestinal parasitosis, giardiasis has been reported to be associated with delayed growth in malnourished children under 5 living in low- and middle-income countries. Relevant data on the nutritional status of children aged 0–59 months presenting with moderate-to-severe diarrhea (MSD) and giardia infection were collected from sentinel health facilities of the Global Enteric Multicenter Study’s (GEMS) seven field settings, placed in diverse countries of Sub-Saharan Africa and South Asia between, December 2007 and February 2011. Then, this study analyzed a robust dataset of study participants (n = 22,569). Children having giardiasis with MSD constituted as cases (n = 1786), and those without MSD constituted as controls (n = 3470). Among the seven field sites, symptomatic giardiasis was 15% and 22% in Asian and African sites, respectively, whereas asymptomatic giardia infection (healthy without MSD) in Asian and African sites was 21.7% and 30.7%, respectively. Wasting and underweight were more frequently associated and stunting less often associated with symptomatic giardiasis (for all, p < 0.001). Symptomatic giardiasis had a significant association with worsening of nutritional status in under-five children. Improved socio-economic profile along with proper sanitation and hygienic practices are imperative to enhance child nutritional status, particularly in resource limited settings.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.