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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 <0.001) respectively. The difference of score (mean) between the groups was significant (p<0.001). After categorizing participants' knowledge levels as poor, average and fair, doctors' group has shown to have significant enhancement from level of average to fair compared to that of the nurses. Factors associated with knowledge augmentation of doctors were working in primary health care centers (aOR: 4.22; 95% CI: 1.80, 9.88), job experience less than 5 years (aOR: 4.10; 95% CI: 1.01, 16.63) and experience in caring of family member with COVID-19 morbidity (aOR: 2.06; 95% CI: 1.03, 4.10), after adjusting for relevant covariates such as age, sex and prior COVID-19 illness. 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. [ABSTRACT FROM AUTHOR]

Background Pneumonia is a leading cause of sepsis and mortality in children under 5 years. However, our understanding of the causes of bacteremia in children with pneumonia is limited. Methods We characterized risk factors for bacteremia and death in a cohort of children admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) between 2014 and 2017 with radiographically confirmed pneumonia. Results A total of 4007 young children were hospitalized with pneumonia over the study period. A total of 1814 (45%) had blood cultures obtained. Of those, 108 (6%) were positive. Gram-negative pathogens predominated, accounting for 83 (77%) of positive cultures. These included Pseudomonas (N = 22), Escherichia coli (N = 17), Salmonella enterica (N = 14, including 11 Salmonella Typhi), and Klebsiella pneumoniae (N = 11). Gram-positive pathogens included Pneumococcus (N = 7) and Staphylococcus aureus (N = 6). Resistance to all routinely used empiric antibiotics (ampicillin, gentamicin, ciprofloxacin, and ceftriaxone) for children with pneumonia at the icddr,b was observed in 20 of the 108 isolates. Thirty-one of 108 (29%) children with bacteremia died, compared to 124 of 1706 (7%) who underwent culture without bacteremia (odds ratio [OR], 5.1; 95% confidence interval [CI], 3.3–8.1; P  < .001). Children infected with bacteria resistant to all routinely used empiric antibiotics were at greater risk of death compared to children without bacteremia (OR, 17.3; 95% CI, 7.0–43.1; P  < .001). Conclusions Antibiotic-resistant Gram-negative bacteremia in young children with pneumonia in Dhaka, Bangladesh was associated with a high mortality rate. The pandemic of antibiotic resistance is shortening the lives of young children in Bangladesh, and new approaches to prevent and treat these infections are desperately needed. [ABSTRACT FROM AUTHOR]

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., Competing Interests: SW, SA, NH and JS were employees of PhysIQ, Inc at the time of this analysis., (Copyright: © 2024 Garbern et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)