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Matthew O Wiens,1 Elias Kumbakumba,2 Niranjan Kissoon,3 J Mark Ansermino,4 Andrew Ndamira,2 Charles P Larson51School of Population and Public Health, University of British Columbia, Vancouver, Canada; 2Department of Pediatrics, Mbarara University of Science and Technology, Mbarara, Uganda; 3Department of Pediatrics, BC Children's Hospital and University of British Columbia, Vancouver, Canada; 4Department of Anesthesia, BC Children's Hospital and University of British Columbia, Vancouver, Canada; 5Department of Pediatrics and School of Population and Public Health, University of British Columbia, Vancouver, CanadaAbstract: Sepsis represents the progressive underlying inflammatory pathway secondary to any infectious illness, and ultimately is responsible for most infectious disease-related deaths. Addressing issues related to sepsis has been recognized as an important step towards reducing morbidity and mortality in developing countries, where the majority of the 7.5 million annual deaths in children under 5 years of age are considered to be secondary to sepsis. However, despite its prevalence, sepsis is largely neglected. Application of sepsis definitions created for use in resource-rich countries are neither practical nor feasible in most developing country settings, and alternative definitions designed for use in these settings need to be established. It has also been recognized that the inflammatory state created by sepsis increases the risk of post-discharge morbidity and mortality in developed countries, but exploration of this issue in developing countries is lacking. Research is urgently required to characterize better this potentially important issue.Keywords: children, pediatric sepsis, developing countries

Objective: Intraoperative heart rate is routinely measured independently from the ECG monitor, pulse oximeter, and the invasive blood pressure monitor if available. The presence of artifacts, in one or more of theses signals, especially sustained artifacts, represents a critical challenge for physiological monitoring. When temporal filters are used to suppress sustained artifacts, unwanted delays or signal distortion are often introduced. The aim of this study was to remove artifacts and derive accurate estimates for the heart rate signal by using measurement redundancy.Method: Heart rate measurements from multiple sensors and previous estimates that fall in a short moving window were treated as samples of the same heart rate. A hybrid median filter was used to align these samples into one ordinal series and to select the median as the fused estimate. This method can successfully remove artifacts that are sustained for shorter than half the length of the filter window, or artifacts that are sustained for a longer duration but presented in no more than half of the sensors.Results: The method was tested on both simulated and clinical cases. The performance of the hybrid median filter in the simulated study was compared with that of a two-step estimation process, comprising a threshold-controlled artifact-removal module and a Kalman filter. The estimation accuracy of the hybrid median filter is better than that of the Kalman filter in the presence of artifacts.Conclusions: The hybrid median filter combines the structural and temporal information from two or more sensors and generates a robust estimate of heart rate without requiring strict assumptions about the signal's characteristics. This method is intuitive, computationally simple, and the performance can be easily adjusted. These considerable benefits make this method highly suitable for clinical use. [ABSTRACT FROM AUTHOR]

Purpose: The automated recognition of critical clinical events by physiological monitors is a challenging task exacerbated by a lack of standardized and clinically relevant threshold criteria. The objective of this investigation was to develop consensus for such criteria regarding the identification of three ventilatory events: disconnection or significant leak in the anesthesia circuit, decreased lung compliance or increased resistance, and anesthetic overdose from inhaled anesthetics.Methods: We individually administered a structured interview to five expert anesthesiologists to gain insight into the cognitive processes used by clinicians to diagnose ventilatory events and to determine the common heuristics (rules of thumb) used in clinical practice. We then used common themes, identified from analysis of the structured interviews, to generate questions for a series of web-based questionnaires. Using a modified Delphi technique, iterative questionnaire administration facilitated rapid consensus development on the thresholds for the specific rules used to identify ventilatory events.Results: A threshold for 75% agreement was described for each scenario in a healthy ventilated adult. A disconnection or significant leak in the anesthesia circuit is diagnosed with peak airway pressure (< 5 cm H2O or change of 15 cm H2O), ETCO2 (0 mmHg, 40% drop, or value below 10 mmHg for a duration of 20 sec), and inspired-expired volume difference (300 mL). Increased resistance or decreased lung compliance is diagnosed with high peak airway pressure (40 cm H2O or a 20 cm H2O change), asymmetry of capnogram, and changes in measured compliance or resistance. Anesthetic overdose from inhaled anesthetics is diagnosed with high end-tidal anesthetic agent concentration (2 MAC in a patient less than 60 yr of age or 1.75 MAC in a patient over 60 yr of age), low systolic blood pressure (below 60 mmHg), and low modified electroencephalogram (bispectral index or entropy).Conclusion: This investigation has provided a set of consensus-based criteria for developing rules for the identification of three critical ventilatory events and has presented insight into the decision heuristics used by clinicians. [ABSTRACT FROM AUTHOR]

Anesthesiologists in the operating room are unable to constantly monitor all data generated by physiological monitors. They are further distracted by clinical and educational tasks. An expert system would ideally provide assistance to the anesthesiologist in this data-rich environment. Clinical monitoring expert systems have not been widely adopted, as traditional methods of knowledge encoding require both expert medical and programming skills, making knowledge acquisition difficult. A software application was developed for use as a knowledge authoring tool for physiological monitoring. This application enables clinicians to create knowledge rules without the need of a knowledge engineer or programmer. These rules are designed to provide clinical diagnosis, explanations and treatment advice for optimal patient care to the clinician in real time. By intelligently combining data from physiological monitors and demographical data sources the expert system can use these rules to assist in monitoring the patient. The knowledge authoring process is simplified by limiting connective relationships between rules. The application is designed to allow open collaboration between communities of clinicians to build a library of rules for clinical use. This design provides clinicians with a system for parameter surveillance and expert advice with a transparent pathway of reasoning. A usability evaluation demonstrated that anesthesiologists can rapidly develop useful rules for use in a predefined clinical scenario. [ABSTRACT FROM AUTHOR]

Objectives: Each year, 5.3 million children under 5 years of age die in low-resource settings, often due to delayed recognition of disease severity, inadequate treatment, or a lack of supplies. We describe the use of a comprehensive digital facility-readiness survey tool, recently developed by the Pediatric Sepsis Data CoLaboratory, which aims to identify target areas for quality improvement related to pediatric emergency and critical care., Methods: Facility-readiness surveys were conducted at six sub-Saharan African hospitals providing pediatric emergency and critical care in Uganda (n = 4) and Cameroon (n = 2). The tool is a 2-phase survey to assess readiness to provide pediatric essential emergency and critical care: (1) an "environmental scan," focusing on infrastructure, availability, and functionality of resources, and (2) an "observational scan" assessing the quality and safety of care through direct observation of patients receiving treatment for common diseases. Data were captured in a mobile application and the findings analyzed descriptively., Results: Varying levels of facility readiness to provide pediatric emergency care were observed. Only 1 of 6 facilities had a qualified staff member to assess children for danger signs upon arrival, and only 2 of 6 had staff with skills to manage emergency conditions. Only 21% of essential medicines required for pediatric emergency and critical care were available at all six facilities. Most facilities had clean running water and soap or disinfectants, but most also experienced interruptions to their electricity supply. Less than half of patients received an appropriate discharge note and fewer received counseling on postdischarge care; follow-up was arranged in less than a quarter of cases., Conclusions: These pilot findings indicate that facilities are partially equipped and ready to provide pediatric emergency and critical care. This facility-readiness tool can be utilized in low-resource settings to assist hospital administrators and policymakers to determine priority areas to improve quality of care for the critically ill child., Competing Interests: Disclosure: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The aim of this "Technical Note" is to inform the pediatric critical care data research community about the "2024 Pediatric Sepsis Data Challenge." This competition aims to facilitate the development of open-source algorithms to predict in-hospital mortality in Ugandan children with sepsis. The challenge is to first develop an algorithm using a synthetic training dataset, which will then be scored according to standard diagnostic testing criteria, and then be evaluated against a nonsynthetic test dataset. The datasets originate from admissions to six hospitals in Uganda (2017-2020) and include 3837 children, 6 to 60 months old, who were confirmed or suspected to have a diagnosis of sepsis. The synthetic dataset was created from a random subset of the original data. The test validation dataset closely resembles the synthetic dataset. The challenge should generate an optimal model for predicting in-hospital mortality. Following external validation, this model could be used to improve the outcomes for children with proven or suspected sepsis in low- and middle-income settings., Competing Interests: Ms. Huxford’s institution received funding from Grand Challenges Canada, Thrasher Research Fund, BC Children’s Hospital Foundation, and Mining4Life. Dr. Businge received funding from the Pediatric Sepsis Data CoLaboratory, the World Federation of Pediatric Intensive and Critical Care Societies, the University of British Columbia, and the BC Children’s Hospital Foundation. Dr. Komugisha received support for article research from Grand Challenges Canada. Dr. Tayebwa received funding from the Mbarara University of Science and Technology. Dr. Kamaleswaran received support for article research from the National Institutes of Health. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.)

Sub-Saharan Africa accounts for two-thirds of the global burden of maternal and newborn deaths. Adverse outcomes among postpartum women and newborns occurring in the first six weeks of life are often related, though data co-examining patients are limited. This study is an exploratory analysis describing the epidemiology of postnatal complications among postpartum women and newborns following facility birth and discharge in Mbarara, Uganda. This single-site prospective cohort observational study enrolled postpartum women following facility-based delivery. To capture health information about both the postpartum women and newborns, data was collected and categorized according to domains within the continuum of care including (1) social and demographic, (2) pregnancy history and antenatal care, (3) delivery, (4) maternal discharge, and (5) newborn discharge. The primary outcomes were readmission and mortality within the six-week postnatal period as defined by the WHO. Multivariable logistic regression was used to identify risk factors. Among 2930 discharged dyads, 2.8% and 9.0% of women and newborns received three or more postnatal visits respectively. Readmission and deaths occurred among 108(3.6%) and 25(0.8%) newborns and in 80(2.7%) and 0(0%) women, respectively. Readmissions were related to sepsis/infection in 70(88%) women and 68(63%) newborns. Adjusted analysis found that caesarean delivery (OR:2.91; 95%CI:1.5-6.04), longer travel time to the facility (OR:1.54; 95%CI:1.24-1.91) and higher maternal heart rate at discharge (OR:1.02; 95%CI:1.00-1.01) were significantly associated with maternal readmission. Discharge taken on all patients including maternal haemoglobin (per g/dL) (OR:0.90; 95%CI:0.82-0.99), maternal symptoms (OR:1.76; 95%CI:1.02-2.91), newborn temperature (OR:1.66; 95%CI:1.28-2.13) and newborn heart rate at (OR:1.94; 95%CI:1.19-3.09) were risk factors among newborns. Readmission and death following delivery and discharge from healthcare facilities is still a problem in settings with low rates of postnatal care visits for both women and newborns. Strategies to identify vulnerable dyads and provide better access to follow-up care, are urgently required., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pillay 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.)

Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment (ETAT) guidelines and Smart Triage (ST) models (ST model with independent triggers, and recalibrated ST model with independent triggers) in categorizing children into emergency, priority, and non-urgent triage categories. Sankey diagrams were used to visualize the distribution of children into similar or different triage categories by ETAT and ST models. Sensitivity, specificity, negative and positive predictive values for mortality and admission were calculated. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified 513 (9.2%) children into the emergency category compared to 1163 (20.8%) and 1161 (20.7%) by the ST model with independent triggers and recalibrated model with independent triggers, respectively. ETAT categorized 3089 (55.1%) children as non-urgent compared to 2097 (37.4%) and 2617 (46.7%) for the respective ST models. ETAT classified 191/395 (48.4%) admitted patients as emergencies compared to more than half by all the ST models. ETAT and ST models classified 25/49 (51%) and 39/49 (79.6%) deceased children as emergencies. Sensitivity for admission and mortality was 48.4% and 51% for ETAT and 74.9% and 79.6% for the ST models, respectively. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers and performs comparably to ETAT. Evaluation of Smart Triage in other contexts and comparison to other triage systems is required., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kamau 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.)

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79-0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55-0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%-87%) and 68% (58%-76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model., Competing Interests: John Mark Ansermino serves as a section editor for PLOS Digital Health., (Copyright: © 2024 Zhang 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.)

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kigo 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.)

In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wiens 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.)

Background: Under-five mortality remains concentrated in resource-poor countries. Post-discharge mortality is becoming increasingly recognized as a significant contributor to overall child mortality. With a substantial recent expansion of research and novel data synthesis methods, this study aims to update the current evidence base by providing a more nuanced understanding of the burden and associated risk factors of pediatric post-discharge mortality after acute illness., Methods: Eligible studies published between January 1, 2017 and January 31, 2023, were retrieved using MEDLINE, Embase, and CINAHL databases. Studies published before 2017 were identified in a previous review and added to the total pool of studies. Only studies from countries with low or low-middle Socio-Demographic Index with a post-discharge observation period greater than seven days were included. Risk of bias was assessed using a modified version of the Joanna Briggs Institute critical appraisal tool for prevalence studies. Studies were grouped by patient population, and 6-month post-discharge mortality rates were quantified by random-effects meta-analysis. Secondary outcomes included post-discharge mortality relative to in-hospital mortality, pooled risk factor estimates, and pooled post-discharge Kaplan-Meier survival curves. PROSPERO study registration: #CRD42022350975., Findings: Of 1963 articles screened, 42 eligible articles were identified and combined with 22 articles identified in the previous review, resulting in 64 total articles. These articles represented 46 unique patient cohorts and included a total of 105,560 children. For children admitted with a general acute illness, the pooled risk of mortality six months post-discharge was 4.4% (95% CI: 3.5%-5.4%, I 2  = 94.2%, n = 11 studies, 34,457 children), and the pooled in-hospital mortality rate was 5.9% (95% CI: 4.2%-7.7%, I 2  = 98.7%, n = 12 studies, 63,307 children). Among disease subgroups, severe malnutrition (12.2%, 95% CI: 6.2%-19.7%, I 2  = 98.2%, n = 10 studies, 7760 children) and severe anemia (6.4%, 95% CI: 4.2%-9.1%, I 2  = 93.3%, n = 9 studies, 7806 children) demonstrated the highest 6-month post-discharge mortality estimates. Diarrhea demonstrated the shortest median time to death (3.3 weeks) and anemia the longest (8.9 weeks). Most significant risk factors for post-discharge mortality included unplanned discharges, severe malnutrition, and HIV seropositivity., Interpretation: Pediatric post-discharge mortality rates remain high in resource-poor settings, especially among children admitted with malnutrition or anemia. Global health strategies must prioritize this health issue by dedicating resources to research and policy innovation., Funding: No specific funding was received., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Introduction: In low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility., Methods: This prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values., Discussion: The current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes., Clinical Trial Registration: https://clinicaltrials.gov/, identifier (NCT05730387)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors JN, PML, MV, JMA, and MOW declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2023 Wiens, Trawin, Pillay, Nguyen, Komugisha, Kenya-Mugisha, Namala, Bebell, Ansermino, Kissoon, Payne, Vidler, Christoffersen-Deb, Lavoie and Ngonzi.)

Background: Reducing child mortality in low-income countries is constrained by a lack of vital statistics. In the absence of such data, verbal autopsies provide an acceptable method to determining attributable causes of death. The objective was to assess potential causes of pediatric postdischarge mortality in children younger than age 5 years (under-5) originally admitted for suspected sepsis using verbal autopsies., Methods: Secondary analysis of verbal autopsy data from children admitted to 6 hospitals across Uganda from July 2017 to March 2020. Structured verbal autopsy interviews were conducted for all deaths within 6 months after discharge. Two physicians independently classified a primary cause of death, up to 4 alternative causes, and up to 5 contributing conditions using the Start-Up Mortality List, with discordance resolved by consensus., Results: Verbal autopsies were completed for 361 (98.6%) of the 366 (5.9%) children who died among 6191 discharges (median admission age: 5.4 months [interquartile range, 1.8-16.7]; median time to mortality: 28 days [interquartile range, 9-74]). Most deaths (62.3%) occurred in the community. Leading primary causes of death, assigned in 356 (98.6%) of cases, were pneumonia (26.2%), sepsis (22.1%), malaria (8.5%), and diarrhea (7.9%). Common contributors to death were malnutrition (50.5%) and anemia (25.7%). Reviewers were less confident in their causes of death for neonates than older children (P < .05)., Conclusions: Postdischarge mortality frequently occurred in the community in children admitted for suspected sepsis in Uganda. Analyses of the probable causes for these deaths using verbal autopsies suggest potential areas for interventions, focused on early detection of infections, as well as prevention and treatment of underlying contributors such as malnutrition and anemia., (Copyright © 2023 by the American Academy of Pediatrics.)

The World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) guidelines recognize the importance of discharge planning to ensure continuation of care at home and appropriate follow-up. However, insufficient attention has been paid to post discharge planning in many hospitals contributing to poor implementation. To understand the reasons for suboptimal discharge, we evaluated the pediatric discharge process from hospital admission through the transition to care within the community in Ugandan hospitals. This mixed methods prospective study enrolled 92 study participants in three phases: patient journey mapping for 32 admitted children under-5 years of age with suspected or proven infection, discharge process mapping with 24 pediatric healthcare workers, and focus group discussions with 36 primary caregivers and fathers of discharged children. Data were descriptively and thematically analyzed. We found that the typical discharge process is often not centered around the needs of the child and family. Discharge planning often does not begin until immediately prior to discharge and generally does not include caregiver input. Discharge education and counselling are generally limited, rarely involves the father, and does not focus significantly on post-discharge care or follow-up. Delays in the discharge process itself occur at multiple points, including while awaiting a physical discharge order and then following a discharge order, mainly with billing or transportation issues. Poor peri-discharge care is a significant barrier to optimizing health outcomes among children in Uganda. Process improvements including initiation of early discharge planning, improved communication between healthcare workers and caregivers, as well as an increased focus on post-discharge care, are key to ensuring safe transitions from facility-based care to home-based care among children recovering from severe illness., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kabajaasi 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.)

Objective: To inform digital health design by evaluating diagnostic test properties of antenatal blood pressure (BP) outputs and levels to identify women at risk of adverse outcomes., Design: Planned secondary analysis of cluster randomised trials., Setting: India, Pakistan, Mozambique., Population: Women with in-community BP measurements and known pregnancy outcomes., Methods: Blood pressure was defined by its outputs (systolic and/or diastolic, systolic only, diastolic only or mean arterial pressure [calculated]) and level: normotension-1 (<135/85 mmHg), normotension-2 (135-139/85-89 mmHg), non-severe hypertension (140-149/90-99 mmHg; 150-154/100-104 mmHg; 155-159/105-109 mmHg) and severe hypertension (≥160/110 mmHg). Dose-response (adjusted risk ratio [aRR]) and diagnostic test properties (negative [-LR] and positive [+LR] likelihood ratios) were estimated., Main Outcome Measures: Maternal/perinatal composites of mortality/morbidity., Results: Among 21 069 pregnancies, different BP outputs had similar aRR, -LR, and +LR for adverse outcomes. No BP level (even normotension-1) was associated with low risk (all -LR ≥0.20). Across outcomes, risks rose progressively with higher BP levels above normotension-1. For each of maternal central nervous system events and stillbirth, BP ≥155/105 mmHg showed at least good diagnostic test performance (+LR ≥5.0) and BP ≥135/85 mmHg at least fair performance, similar to BP ≥140/90 mmHg (+LR 2.0-4.99)., Conclusions: In the community, normal BP values do not provide reassurance about subsequent adverse outcomes. Given the similar performance of BP cut-offs of 135/85 and 140/90 mmHg for hypertension, and 155/105 and 160/110 mmHg for severe hypertension, digital decision support for women in the community should consider using these lower thresholds., (© 2023 The Authors. BJOG: An International Journal of Obstetrics and Gynaecology published by John Wiley & Sons Ltd.)

Background: Sepsis, characterized by organ dysfunction due to presumed or proven infection, has a case-fatality over 20% in severe cases in low-and-middle income countries. Early diagnosis and treatment have proven benefits, prompting our implementation of Smart Triage at Jinja Regional Referral Hospital in Uganda, a program that expedites treatment through a data-driven triage platform. We conducted a cost-effectiveness analysis of Smart Triage to explore its impact on patients and inform multicenter scale up., Methods: The parent clinical trial for Smart Triage was pre-post in design, using the proportion of children receiving sepsis treatment within one hour as the primary outcome, a measure linked to mortality benefit in existing literature. We used a decision-analytic model with Monte Carlo simulation to calculate the cost per year-of-life-lost (YLL) averted of Smart Triage from societal, government, and patient perspectives. Healthcare utilization and lost work for seven days post-discharge were translated into costs and productivity losses via secondary linkage data., Results: In 2021 United States dollars, Smart Triage requires an annuitized program cost of only $0.05 per child, but results in $15.32 saved per YLL averted. At a willingness-to-pay threshold of only $3 per YLL averted, well below published cost-effectiveness threshold estimates for Uganda, Smart Triage approaches 100% probability of cost-effectiveness over the baseline manual triage system. This cost-effectiveness was observed from societal, government, and patient perspectives. The cost-effectiveness observed was driven by a reduction in admission that, while explainable by an improved triage mechanism, may also be partially attributable to changes in healthcare utilization influenced by the coronavirus pandemic. However, Smart Triage remains cost-effective in sensitivity analyses introducing a penalty factor of up to 50% in the reduction in admission., Conclusion: Smart Triage's ability to both save costs and avert YLLs indicates that patients benefit both economically and clinically, while its high probability of cost-effectiveness strongly supports multicenter scale up. Areas for further research include the incorporation of years lived with disability when sepsis disability weights in low-resource settings become available and analyzing budget impact during multicenter scale up., Trial Registration: NCT04304235 (registered on 11/03/2020, clinicaltrials.gov)., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: In low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of oxygen saturation (SpO2). However, the accuracy and reliability of pulse oximeters for spot measurements have not been determined. We evaluated the repeatability of spot measurements and the ideal observation time to guide recommendations during spot check measurements., Methods: Two 1-minute measurements were taken for the 3,903 subjects enrolled in the study conducted April 2020-January 2022 in Uganda, collecting 1 Hz SpO2 and signal quality index (SQI) data. The repeatability between the 2 measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO2 values for each recording (any quality, Q1) and again with a quality filter only using seconds with SQI 90% or higher (good quality, Q2). The ICC was also recalculated for both conditions of Q1 and Q2 using the initial 5 seconds, then the initial 10 seconds, and continuing with 5-second increments up to the full 60 seconds. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI of more than 70% (Q3)., Results: The repeatability ICC with condition Q1 was 0.591 (95% confidence interval [CI]=0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI=0.169, 0.230). Filtering with Q2, the whole-minute ICC was 0.855 (95% CI=0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI=0.901, 0.914)., Conclusions: Training guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurements., (© Asdo et al.)

Background: Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death., Methods: In this prospective, multisite, observational cohort study, we recruited and consecutively enrolled children aged 0-60 months admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. Suspected sepsis was defined as the need for admission due to a suspected or proven infectious illness. At admission, trained study nurses systematically collected data on clinical variables, sociodemographic variables, and baseline characteristics with encrypted study tablets. Participants were followed up for 6 months after discharge by field officers who contacted caregivers at 2 months and 4 months after discharge by telephone and at 6 months after discharge in person to measure vital status, health-care seeking after discharge, and readmission details. We assessed 6-month mortality after hospital discharge among those discharged alive, with verbal autopsies conducted for children who had died after hospital discharge., Findings: Between July 13, 2017, and March 30, 2020, 16 991 children were screened for eligibility. 6545 children (2927 [44·72%] female children and 3618 [55·28%] male children) were enrolled and 6191 were discharged from hospital alive. 6073 children (2687 [44·2%] female children and 3386 [55·8%] male children) completed follow-up. 366 children died in the 6-month period after discharge (weighted mortality rate 5·5%). Median time from discharge to death was 28 days (IQR 9-74). For the 360 children for whom location of death was documented, deaths occurred at home (162 [45·0%]), in transit to care (66 [18·3%]), or in hospital (132 [36·7%]) during a subsequent readmission. Death after hospital discharge was strongly associated with weight-for-age Z scores less than -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7-5·8 vs a Z score of >-2), discharge or referral to a higher level of care (7·3, 5·6-9·5), and unplanned discharge (3·2, 2·5-4·0). Hazard ratios (HRs) for severe anaemia (<7g/dL) increased with time since discharge, from 1·7 (95% CI 0·9-3·0) for death occurring in the first time tertile to 5·2 (3·1-8·5) in the third time tertile. HRs for some discharge vulnerabilities decreased significantly with increasing time since discharge, including unplanned discharge (from 4.5 [2·9-6·9] in the first tertile to 2·0 [1·3-3·2] in the third tertile) and poor feeding status (from 7·7 [5·4-11·0] to 1·84 [1·0-3·3]). Age interacted with several variables, including reduced weight-for-age Z score, severe anaemia, and reduced admission temperature., Interpretation: Paediatric mortality following hospital discharge after suspected sepsis is common, with diminishing, although persistent, risk during the first 6 months after discharge. Efforts to improve outcomes after hospital discharge are crucial to achieving Sustainable Development Goal 3.2 (ending preventable childhood deaths under age 5 years)., Funding: Grand Challenges Canada, Thrasher Research Fund, BC Children's Hospital Foundation, and Mining4Life., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Apnoea of prematurity (AOP) is a common condition among preterm infants. Methylxanthines, such as caffeine and aminophylline/theophylline, can help prevent and treat AOP. Due to its physiological benefits and fewer side effects, caffeine citrate is recommended for the prevention and treatment of AOP. However, caffeine citrate is not available in most resource-constrained settings (RCS) due to its high cost. Challenges in RCS using caffeine citrate to prevent AOP include identifying eligible preterm infants where gestational age is not always known and the capability for continuous monitoring of vital signs to readily identify apnoea. We aim to develop an evidence-based care bundle that includes caffeine citrate to prevent and manage AOP in tertiary healthcare facilities in Kenya., Methods: This protocol details a prospective mixed-methods clinical feasibility study on using caffeine citrate to manage apnoea of prematurity in a single facility tertiary-care newborn unit (NBU) in Nairobi, Kenya. This study will include a 4-month formative research phase followed by the development of an AOP clinical-care-bundle prototype over 2 months. In the subsequent 4 months, implementation and improvement of the clinical-care-bundle prototype will be undertaken. The baseline data will provide contextualised insights on care practices within the NBU that will inform the development of a context-sensitive AOP clinical-care-bundle prototype. The clinical care bundle will be tested and refined further during an implementation phase of the quality improvement initiative using a PDSA framework underpinned by quantitative and qualitative clinical audits and stakeholders' engagement. The quantitative component will include all neonates born at gestation age < 34 weeks and any neonate prescribed aminophylline or caffeine citrate admitted to the NBU during the study period., Discussion: There is a need to develop evidence-based and context-sensitive clinical practice guidelines to standardise and improve the management of AOP in RCS. Concerns requiring resolution in implementing such guidelines include diagnosis of apnoea, optimal timing, dosing and administration of caffeine citrate, standardisation of monitoring devices and alarm limits, and discharge protocols. We aim to provide a feasible standardised clinical care bundle for managing AOP in low and middle-income settings., (© 2023. The Author(s).)

Background: In low- and middle-income countries, approximately two thirds of maternal deaths occur in the postpartum period. Yet, care for women beyond 24 h after discharge is limited. The objective of this systematic review is to summarize current evidence on socio-demographic and clinical risk factors for (1) postpartum mortality and (2) postpartum hospital readmission., Methods: A combination of keywords and subject headings (i.e. MeSH terms) for postpartum maternal mortality or readmission were searched. Articles published up to January 9, 2021 were identified in MEDLINE, EMBASE, and CINAHL databases, without language restrictions. Studies reporting socio-demographic or clinical risk factors for postpartum mortality or readmission within six weeks of delivery among women who delivered a livebirth in a low- or middle-income country were included. Data were extracted independently by two reviewers based on study characteristics, population, and outcomes. Included studies were assessed for quality and risk of bias using the Downs and Black checklist for ratings of randomized and non-randomized studies., Results: Of 8783 abstracts screened, seven studies were included (total N = 387,786). Risk factors for postpartum mortality included Caesarean mode of delivery, nulliparity, low or very low birthweight, and shock upon admission. Risk factors for postpartum readmission included Caesarean mode of delivery, HIV positive serostatus, and abnormal body temperature., Conclusions: Few studies reported individual socio-demographic or clinical risk factors for mortality or readmission after delivery in low- and middle-income countries; only Caesarean delivery was consistently reported. Further research is needed to identify factors that put women at greatest risk of post-discharge complications and mortality. Understanding post-discharge risk would facilitate targeted postpartum care and reduce adverse outcomes in women after delivery., Trial Registration: PROSPERO registration number: CRD42018103955., (© 2023. The Author(s).)

Accurate clinical sensors and devices are essential to support optimal medical decision-making, and accuracy can be demonstrated through the conduct of clinical validation studies using validated reference sensors and/or devices for comparison. Typically unmeasurable, the true reference value can be substituted with an accepted physiological measurement with an associated uncertainty. We describe a basic model of measurement uncertainty that specifies the factors that may degrade the accuracy of an observed measurement value from a sensor, and we detail validation study design strategies that may be used to quantify and minimize these uncertainties. In addition, we describe a model that extends the observed measurement uncertainty to the resultant clinical decision and the factors that may impact the uncertainty of this decision. Clinical validation studies should be designed to estimate and minimize uncertainty that is unrelated to the sensor accuracy. The contribution of measurement observation uncertainty to clinical decision-making should be minimized but also acknowledged and incorporated into the clinical decision-making process.

Background: More than 50 countries, mainly in Sub-Saharan Africa and South Asia, are not on course to meet the neonatal and under-five mortality target set by the Sustainable Development Goals (SDGs) for the year 2030. One important, yet neglected, aspect of child mortality rates is deaths occurring during the post-discharge period. For children living in resource-poor countries, the rate of post-discharge mortality within the first several months after discharge is often as high as the rates observed during the initial admission period. This has generally been observed within the context of acute illness and has been closely linked to underlying conditions such as malnutrition, HIV, and anemia. These post-discharge mortality rates tend to be underreported and present a major oversight in the efforts to reduce overall child mortality. This review will explore recurrent illness following discharge through determination of rates of, and risk factors for, pediatric post-discharge mortality in resource-poor settings., Methods: Eligible studies will be retrieved using MEDLINE, EMBASE, and CINAHL databases. Only studies with a post-discharge observation period of more than 7 days following discharge will be eligible for inclusion. Secondary outcomes will include post-discharge mortality relative to in-hospital mortality, overall readmission rates, pooled estimates of risk factors (e.g. admission details vs discharge factors, clinical vs social factors), pooled post-discharge mortality Kaplan-Meier survival curves, and outcomes by disease subgroups (e.g. malnutrition, anemia, general admissions). A narrative description of the included studies will be synthesized to categorize commonly affected patient population categories and a random-effects meta-analysis will be conducted to quantify overall post-discharge mortality rates at the 6-month time point., Discussion: Post-discharge mortality contributes to global child mortality rates with a greater burden of deaths occurring in resource-poor settings. Literature concentrated on child mortality published over the last decade has expanded to focus on the fatal outcomes of children post-discharge and associated risk factors. The results from this systematic review will inform current policy and interventions on the epidemiological burden of post-discharge mortality and morbidity following acute illness among children living in resource-poor settings., Systematic Review Registration: PROSPERO Registration ID: CRD42022350975., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Chaudhry 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.)

Background : The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained self-supervised learning (SSL) models for automatic feature extraction from raw photoplethysmography (PPG) obtained using a pulse oximeter, with the aim of predicting paediatric hospitalization.  Methods : We compared logistic regression models fitted using features extracted using SSL with models trained using both clinical and SSL features. In addition, we compared end-to-end deep learning models initialized randomly or using weights from the SSL models. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results : Logistic regression models were more predictive of hospitalization when trained on features extracted using labelled PPG signals only compared to SSL models trained on both labelled and unlabelled signals (AUC 0.83 vs 0.80). However, features extracted using SSL model trained on both labelled and unlabelled PPG signals were more predictive of hospitalization when concatenated with clinical features (AUC 0.89 vs 0.87). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions : This study shows that SSL can extract features from PPG signals that are predictive of hospitalization or initialize end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Mwaniki P et al.)

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing., (© 2022. The Author(s).)

Introduction: While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. This scoping review will critically analyse the current evidence on the design and performance of automated digital technologies to predict paediatric sepsis, to advance their development and integration within clinical settings., Methods and Analysis: This scoping review will follow Arksey and O'Malley's framework, conducted between February and December 2022. We will further develop the protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. We plan to search the following databases: Association of Computing Machinery (ACM) Digital Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Google Scholar, Institute of Electric and Electronic Engineers (IEEE), PubMed, Scopus and Web of Science. Studies will be included on children >90 days postnatal to <21 years old, predicted to have or be at risk of developing sepsis by a digitalised model or algorithm designed for a clinical setting. Two independent reviewers will complete the abstract and full-text screening and the data extraction. Thematic analysis will be used to develop overarching concepts and present the narrative findings with quantitative results and descriptive statistics displayed in data tables., Ethics and Dissemination: Ethics approval for this scoping review study of the available literature is not required. We anticipate that the scoping review will identify the current evidence and design characteristics of digital prediction technologies for the timely and accurate prediction of paediatric sepsis and factors influencing clinical integration. We plan to disseminate the preliminary findings from this review at national and international research conferences in global and digital health, gathering critical feedback from multidisciplinary stakeholders. SCOPING REVIEW REGISTRATION: https://osf.io/veqha/?view&#95;only=f560d4892d7c459ea4cff6dcdfacb086., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Early and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage., Methods: This was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation., Results: The model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit ( p ) = -32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (-0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%., Conclusion: In a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress., Competing Interests: NK serves as a Specialty Chief Editor for Frontiers in Pediatrics. The peer-review process was guided by an independent editor, and the authors have no other competing interests to declare., (© 2022 Mawji, Li, Dunsmuir, Komugisha, Novakowski, Wiens, Vesuvius, Kissoon and Ansermino.)

Background: Effective triage at hospitals can improve outcomes for children globally by helping identify and prioritize care for those most at-risk of death. Paper-based pediatric triage guidelines have been developed to support frontline health workers in low-resource settings, but these guidelines can be challenging to implement. Smart Triage is a digital triaging platform for quality improvement (QI) that aims to address this challenge. Smart Triage represents a major cultural and behavioural shift in terms of managing patients at health facilities in low-and middle-income countries. The purpose of this study is to understand user perspectives on the usability, feasibility, and acceptability of Smart Triage to inform ongoing and future implementation., Methods: This was a descriptive qualitative study comprising of face-to-face interviews with health workers (n = 15) at a regional referral hospital in Eastern Uganda, conducted as a sub-study of a larger clinical trial to evaluate Smart Triage (NCT04304235). Thematic analysis was used to assess the usability, feasibility, and acceptability of the platform, focusing on its use in stratifying and prioritizing patients according to their risk and informing QI initiatives implemented by health workers., Results: With appropriate training and experience, health workers found most features of Smart Triage usable and feasible to implement, and reported the platform was acceptable due to its positive impact on reducing the time to treatment for emergency pediatric cases and its use in informing QI initiatives within the pediatric ward. Several factors that reduced the feasibility and acceptability were identified, including high staff turnover, a lack of medical supplies at the hospital, and challenges with staff attitudes., Conclusion: Health workers can use the Smart Triage digital triaging platform to identify and prioritize care for severely ill children and improve quality of care at health facilities in low-resource settings. Future innovation is needed to address identified feasibility and acceptability challenges; however, this platform could potentially address some of the challenges to implementing current paper-based systems., (© 2022. The Author(s).)

Objective: Infectious diseases, including pneumonia, malaria, and diarrheal diseases, are the leading causes of death in children younger than 5 years worldwide. The vast majority of these deaths occur in resource-limited settings where there is significant variation in the availability and type of human, physical, and infrastructural resources. The ability to identity gaps in healthcare systems that may hinder their ability to deliver care is an important step to determining specific interventions for quality improvement. Our study objective was to develop a comprehensive, digital, open-access health facility survey to assess facility readiness to provide pediatric critical care in resource-limited settings (eg, low- and lower middle-income countries)., Methods: A literature review of existing facility assessment tools and global guidelines was conducted to generate a database of survey questions. These were then mapped to one of the following 8 domains: hospital statistics, services offered, operational flow, facility infrastructure, staff and training, medicines and equipment, diagnostic capacity, and quality of clinical care. A 2-phase survey was developed and an iterative review process of the survey was undertaken with 12 experts based in low- and middle-income countries. This was built into the REDCap Mobile Application for electronic data capture., Results: The literature review process yielded 7 facility assessment tools and 7 global guidelines for inclusion. After the iterative review process, the final survey consisted of 11 sections with 457 unique questions in the first phase, "environmental scan," focusing on the infrastructure, availability, and functionality of resources, and 3 sections with 131 unique questions in the second phase, "observation scan," focusing on the level of clinical competency., Conclusions: A comprehensive 2-phase survey was created to evaluate facility readiness for pediatric critical care. Results will assist hospital administrators and policymakers to determine priority areas for quality improvement, enabling them to implement a Plan-Do-Study-Act cycle to improve care for the critically ill child., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community., Competing Interests: JMA serves as a section editor for PLOS Digital Health. The peer-review process was guided by an independent editor, and the authors have no other competing interests to declare., (Copyright: © 2022 Mawji 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.)

Clinically feasible multiparameter continuous physiological monitoring technologies are needed for use in resource-constrained African healthcare facilities to allow for early detection of critical events and timely intervention for major morbidities in high-risk neonates. We conducted a prospective clinical feasibility study of a novel multiparameter continuous physiological monitoring technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of Sibel's Advanced Neonatal Epidermal (ANNE) technology to reference technologies, including Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO 2 ) measurements and Spengler's Tempo Easy non-contact infrared thermometer for temperature measurements. We evaluated key performance criteria such as up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies in an uncontrolled, real-world setting. Between September 15 and December 15, 2020, we collected and analyzed 503 h of ANNE data from 109 enrolled neonates. ANNE's up-time was 42 (11%) h more for HR, 77 (25%) h more for RR, and 6 (2%) h less for SpO 2 compared to the Rad-97. However, ANNE's ratio of up-time to total attached time was less than Rad-97's for HR (0.79 vs 0.86), RR (0.68 vs. 0.79), and SpO 2 (0.69 vs 0.86). ANNE demonstrated adequate performance in identifying high and low HR and RR and high temperature events; however, showed relatively poor performance for low SpO 2 events. The normalized spread of limits of agreement were 8.4% for HR and 52.2% for RR and the normalized root-mean-square deviation was 4.4% for SpO 2 . Temperature agreement showed a spread of limits of agreement of 2.8 °C. The a priori-identified optimal limits were met for HR and temperature but not for RR or SpO 2 . ANNE was clinically feasible for HR and temperature but not RR and SpO 2 as demonstrated by the technology's up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies., (© 2022. The Author(s).)

Background: Neonatal multiparameter continuous physiological monitoring (MCPM) technologies assist with early detection of preventable and treatable causes of neonatal mortality. Evaluating accuracy of novel MCPM technologies is critical for their appropriate use and adoption., Methods: We prospectively compared the accuracy of Sibel's Advanced Neonatal Epidermal (ANNE) technology with Masimo's Rad-97 pulse CO-oximeter with capnography and Spengler's Tempo Easy reference technologies during four evaluation rounds. We compared accuracy of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and skin temperature using Bland-Altman plots and root-mean-square deviation analyses (RMSD). Sibel's ANNE algorithms were optimized between each round. We created Clarke error grids with zones of 20% to aid with clinical interpretation of HR and RR results., Results: Between November 2019 and August 2020 we collected 320 hours of data from 84 neonates. In the final round, Sibel's ANNE technology demonstrated a normalized bias of 0% for HR and 3.1% for RR, and a non-normalized bias of -0.3% for SpO2 and 0.2°C for temperature. The normalized spread between 95% upper and lower limits-of-agreement (LOA) was 4.7% for HR and 29.3% for RR. RMSD for SpO2 was 1.9% and 1.5°C for temperature. Agreement between Sibel's ANNE technology and the reference technologies met the a priori-defined thresholds for 95% spread of LOA and RMSD. Clarke error grids showed that all HR and RR observations were within a 20% difference., Conclusion: Our findings suggest acceptable agreement between Sibel's ANNE and reference technologies. Clinical effectiveness, feasibility, usability, acceptability, and cost-effectiveness investigations are necessary for large-scale implementation., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Shuai Xu is Founder and Chief Executive Officer at Sibel Health; all other authors declare no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings., Methods: In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation., Results: Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference., Conclusion: Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates., Trial Registration Number: NCT03920761., Competing Interests: Competing interests: YSH and RK are employed by EarlySense. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Multiparameter continuous physiological monitoring (MCPM) technologies are critical in the clinical management of high-risk neonates; yet, these technologies are frequently unavailable in many African healthcare facilities. We conducted a prospective clinical feasibility study of EarlySense's novel under-mattress MCPM technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of EarlySense's technology to Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR) and respiratory rate (RR) measurements using up-time, clinical event detection performance, and accuracy. Between September 15 and December 15, 2020, we collected and analyzed 470 hours of EarlySense data from 109 enrolled neonates. EarlySense's technology's up-time per neonate was 2.9 (range 0.8, 5.3) hours for HR and 2.1 (range 0.9, 4.0) hours for RR. The difference compared to the reference was a median of 0.6 (range 0.1, 3.1) hours for HR and 0.8 (range 0.1, 2.9) hours for RR. EarlySense's technology identified high HR and RR events with high sensitivity (HR 81%; RR 83%) and specificity (HR 99%; RR 83%), but was less sensitive for low HR and RR (HR 0%; RR 14%) although maintained specificity (HR 100%; RR 95%). There was a greater number of false negative and false positive RR events than false negative and false positive HR events. The normalized spread of limits of agreement was 9.6% for HR and 28.6% for RR, which met the a priori-identified limit of 30%. EarlySense's MCPM technology was clinically feasible as demonstrated by high percentage of up-time, strong clinical event detection performance, and agreement of HR and RR measurements compared to the reference technology. Studies in critically ill neonates, assessing barriers and facilitators to adoption, and costing analyses will be key to the technology's development and potential uptake and scale-up., (© 2022. The Author(s).)

Objective: To assess the feasibility, usability and acceptability of two non-invasive, multiparameter, continuous physiological monitoring (MCPM) technologies for use in neonates within a resource-constrained healthcare setting in sub-Saharan Africa., Design: A qualitative study using in-depth interviews and direct observations to describe healthcare professional and caregiver perspectives and experiences with investigational MCPM technologies from EarlySense and Sibel compared with selected reference technologies., Setting: Pumwani Maternity Hospital is a public, high-volume, tertiary hospital in Nairobi, Kenya., Participants: In-depth interviews were conducted with five healthcare administrators, 12 healthcare providers and 10 caregivers. Direct observations were made of healthcare providers using the technologies on 12 neonates overall., Results: Design factors like non-invasiveness, portability, ease-of-use and ability to measure multiple vital signs concurrently emerged as key themes supporting the usability and acceptability of the investigational technologies. However, respondents also reported feasibility challenges to implementation, including overcrowding in the neonatal unit, lack of reliable access to electricity and computers, and concerns about cost and maintenance needs. To improve acceptability, respondents highlighted the need for adequate staffing to appropriately engage caregivers and dispel misconceptions about the technologies., Conclusion: Study participants were positive about the usefulness of the investigational technologies to strengthen clinical care quality and identification of at-risk neonates for better access to timely interventions. These technologies have the potential to improve equity of access to appropriate healthcare services and neonatal outcomes in sub-Saharan African healthcare facilities. However, health system strengthening is also critical to support sustainable uptake of technologies into routine care., Trial Registration Number: NCT03920761., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: Respiratory rate is difficult to measure, especially in neonates who have an irregular breathing pattern. The World Health Organisation recommends a one-minute count, but there is limited data to support this length of observation. We sought to evaluate agreement between the respiratory rate (RR) derived from capnography in neonates, over 15 s, 30 s, 120 s and 300 s, against the recommended 60 s., Methods: Neonates at two hospitals in Nairobi were recruited and had capnograph waveforms recorded using the Masimo Rad 97. A single high quality 5 min epoch was randomly chosen from each subject. For each selected epoch, the mean RR was calculated using a breath-detection algorithm applied to the waveform. The RR in the first 60 s was compared to the mean RR measured over the first 15 s, 30 s, 120 s, full 300 s, and last 60 s. We calculated bias and limits of agreement for each comparison and used Bland-Altman plots for visual comparisons., Results: A total of 306 capnographs were analysed from individual subjects. The subjects had a median gestation age of 39 weeks with slightly more females (52.3%) than males (47.7%). The majority of the population were term neonates (70.1%) with 39 (12.8%) having a primary respiratory pathology. There was poor agreement between all the comparisons based on the limits of agreement [confidence interval], ranging between 11.9 [- 6.79 to 6.23] breaths per minute in the one versus 2 min comparison, and 34.7 [- 17.59 to 20.53] breaths per minute in the first versus last minute comparison. Worsening agreement was observed in plots with higher RRs., Conclusions: Neonates have high variability of RR, even over a short period of time. A slight degradation in the agreement is noted over periods shorter than 1 min. However, this is smaller than observations done 3 min apart in the same subject. Longer periods of observation also reduce agreement. For device developers, precise synchronization is needed when comparing devices to reduce the impact of RR variation. For clinicians, where possible, continuous or repeated monitoring of neonates would be preferable to one time RR measurements., (© 2021. The Author(s).)

Background: Sepsis disproportionately affects children from socioeconomically disadvantaged families in low-resource settings, where care seeking may consume scarce family resources and lead to financial hardships. Those financial hardships may, in turn, contribute to late presentation or failure to seek care and result in high mortality during hospitalization and during the post discharge period, a period of increasingly recognized vulnerability. The purpose of this study is to explore the out-of-pocket costs related to sepsis hospitalizations and post-discharge care among children admitted with sepsis in Uganda., Methods: This mixed-methods study was comprised of focus group discussions (FGD) with caregivers of children admitted for sepsis, which then informed a quantitative cross-sectional household survey to measure out-of-pocket costs of sepsis care both during initial admission and during the post-discharge period. All participants were families of children enrolled in a concurrent sepsis study., Results: Three FGD with mothers (n = 20) and one FGD with fathers (n = 7) were conducted. Three primary themes that emerged included (1) financial losses, (2) time and productivity losses and (3) coping with costs. A subsequently developed cross-sectional survey was completed for 153 households of children discharged following admission for sepsis. The survey revealed a high cost of care for families attending both private and public facilities, although out-of-pocket cost were higher at private facilities. Half of those surveyed reported loss of income during hospitalization and a third sold household assets, most often livestock, to cover costs. Total mean out-of-pocket costs of hospital care and post-discharge care were 124.50 USD and 44.60 USD respectively for those seeking initial care at private facilities and 62.10 USD and 14.60 USD at public facilities, a high sum in a country with widespread poverty., Conclusions: This study reveals that families incur a substantial economic burden in accessing care for children with sepsis., (© 2021. The Author(s).)

Background: Sepsis is a clinical syndrome characterized by organ dysfunction due to presumed or proven infection. Severe cases can have case fatality ratio 25% or higher in low-middle income countries, but early diagnosis and timely treatment have a proven benefit. The Smart Triage program in Jinja Regional Referral Hospital in Uganda will provide expedited sepsis treatment in children through a data-driven electronic patient triage system. To complement the ongoing Smart Triage interventional trial, we propose methods for a concurrent cost-effectiveness analysis of the Smart Triage platform., Methods: We will use a decision-analytic model taking a societal perspective, combining government and out-of-pocket costs, as patients bear a sizeable portion of healthcare costs in Uganda due to the lack of universal health coverage. Previously published secondary data will be used to link healthcare utilization with costs and intermediate outcomes with mortality. We will model uncertainty via probabilistic sensitivity analysis and present findings at various willingness-to-pay thresholds using a cost-effectiveness acceptability curve., Discussion: Our proposed analysis represents a first step in evaluating the cost-effectiveness of an innovative digital triage platform designed to improve clinical outcomes in pediatric sepsis through expediting care in low-resource settings. Our use of a decision analytic model to link secondary costing data, incorporate post-discharge healthcare utilization, and model clinical endpoints is also novel in the pediatric sepsis triage literature for low-middle income countries. Our analysis, together with subsequent analyses modelling budget impact and scale up, will inform future modifications to the Smart Triage platform, as well as motivate scale-up to the district and national levels., Trial Registration: Trial registration of parent clinical trial: NCT04304235, https://clinicaltrials.gov/ct2/show/NCT04304235. Registered 11 March 2020., Competing Interests: The authors have declared that no competing interests exist.

Background: Closed-loop control of propofol-remifentanil anesthesia using the processed electroencephalography depth-of-hypnosis index provided by the NeuroSENSE monitor (WAVCNS) has been previously described. The purpose of this placebo-controlled study was to evaluate the performance (percentage time within ±10 units of the setpoint during the maintenance of anesthesia) of a closed-loop propofol-remifentanil controller during induction and maintenance of anesthesia in the presence of a low dose of ketamine., Methods: Following ethical approval and informed consent, American Society of Anesthesiologist (ASA) physical status I-II patients aged 19-54 years, scheduled for elective orthopedic surgery requiring general anesthesia for >60 minutes duration, were enrolled in a double-blind randomized, placebo-controlled, 2-group equivalence trial. Immediately before induction of anesthesia, participants in the ketamine group received a 0.25 mg·kg-1 bolus of intravenous ketamine over 60 seconds followed by a continuous 5 µg·kg-1·min-1 infusion for up to 45 minutes. Participants in the control group received an equivalent volume of normal saline. After the initial study drug bolus, closed-loop induction of anesthesia was initiated; propofol and remifentanil remained under closed-loop control until the anesthetic was tapered and turned off at the anesthesiologist's discretion. An equivalence range of ±8.99% was assumed for comparing controller performance., Results: Sixty patients participated: 41 males, 54 ASA physical status I, with a median (interquartile range [IQR]) age of 29 [23, 38] years and weight of 82 [71, 93] kg. Complete data were available from 29 cases in the ketamine group and 27 in the control group. Percentage time within ±10 units of the WAVCNS setpoint was median [IQR] 86.6% [79.7, 90.2] in the ketamine group and 86.4% [76.5, 89.8] in the control group (median difference, 1.0%; 95% confidence interval [CI] -3.6 to 5.0). Mean propofol dose during maintenance of anesthesia for the ketamine group was higher than for the control group (median difference, 24.9 µg·kg-1·min-1; 95% CI, 6.5-43.1; P = .005)., Conclusions: Because the 95% CI of the difference in controller performance lies entirely within the a priori equivalence range, we infer that this analgesic dose of ketamine did not alter controller performance. Further study is required to confirm the finding that mean propofol dosing was higher in the ketamine group, and to investigate the implication that this dose of ketamine may have affected the WAVCNS., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)

Objective . Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea. Approach . Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models. Main results . Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital. Significance . At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children., (Creative Commons Attribution license.)

Background: Continuous physiological monitoring technologies are important for strengthening hospital care for neonates, particularly in resource-constrained settings, and understanding user perspectives is critical for informing medical technology design, development, and optimization., Objective: This study aims to assess the feasibility, usability, and acceptability of 2 noninvasive, multiparameter, continuous physiological monitoring technologies for use in neonates in an African health care setting., Methods: We assessed 2 investigational technologies from EarlySense and Sibel, compared with the reference Masimo Rad-97 technology through in-depth interviews and direct observations. A purposive sample of health care administrators, health care providers, and caregivers at Aga Khan University Hospital, a tertiary, private hospital in Nairobi, Kenya, were included. Data were analyzed using a thematic approach in NVivo 12 software., Results: Between July and August 2020, we interviewed 12 health care providers, 5 health care administrators, and 10 caregivers and observed the monitoring of 12 neonates. Staffing and maintenance of training in neonatal units are important feasibility considerations, and simple training requirements support the feasibility of the investigational technologies. Key usability characteristics included ease of use, wireless features, and reduced number of attachments connecting the neonate to the monitoring technology, which health care providers considered to increase the efficiency of care. The main factors supporting acceptability included caregiver-highlighted perceptions of neonate comfort and health care respondent technology familiarity. Concerns about the side effects of wireless connections, electromagnetic fields, and mistrust of unfamiliar technologies have emerged as possible acceptability barriers to investigational technologies., Conclusions: Overall, respondents considered the investigational technologies feasible, usable, and acceptable for the care of neonates at this health care facility. Our findings highlight the potential of different multiparameter continuous physiological monitoring technologies for use in different neonatal care settings. Simple and user-friendly technologies may help to bridge gaps in current care where there are many neonates; however, challenges in maintaining training and ensuring feasibility within resource-constrained health care settings warrant further research., International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2019-035184., (©Amy Sarah Ginsburg, Mai-Lei Woo Kinshella, Violet Naanyu, Jessica Rigg, Dorothy Chomba, Jesse Coleman, Bella Hwang, Roseline Ochieng, J Mark Ansermino, William M Macharia. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.10.2021.)

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Coleman J et al.)

Ketamine may affect the reliability of electroencephalographic (EEG) depth-of-hypnosis indices as it affects power in high-frequency EEG components. The purpose of this study was to compare the effects of ketamine on three commonly-used depth-of-hypnosis indices by extending our EEG simulator to allow replay of previously-recorded EEG. Secondary analysis of previously-collected data from a randomized controlled trial of intravenous anesthesia with ketamine: Group 0.5 [ketamine, 0.5 mg kg -1 bolus followed by a 10 mcg kg -1  min -1 infusion], Group 0.25 [ketamine, 0.25 mg kg -1  bolus, 5 mcg kg -1  min -1 infusion], and Control [no ketamine]. EEG data were replayed to three monitors: NeuroSENSE (WAV), Bispectral Index (BIS), and Entropy (SE). Differences in depth-of-hypnosis indices during the initial 15 min after induction of anesthesia were compared between monitors, and between groups. Monitor agreement was evaluated using Bland-Altman analysis. Available data included 45.6 h of EEG recordings from 27 cases. Ketamine was associated with higher depth-of-hypnosis index values measured at 10 min (BIS, χ 2  = 8.01, p = 0.018; SE, χ 2  = 11.44, p = 0.003; WAV, χ 2  = 9.19, p = 0.010), and a higher proportion of index values > 60 for both ketamine groups compared to the control group. Significant differences between monitors were not observed, except between BIS and SE in the control group. Ketamine did not change agreement between monitors. The ketamine-induced increase in depth-of-hypnosis indices was observed consistently across the three EEG monitoring algorithms evaluated. The observed increase was likely caused by a power increase in the beta and gamma bands. However, there were no lasting differences in depth-of-hypnosis reported between the three compared indices., (© 2020. Springer Nature B.V.)

Dose-dependent effects of ketamine on processed electroencephalographic depth-of-hypnosis indices have been reported. Limited data are available for the NeuroSENSE WAV CNS index. Our aim was to establish the feasibility of closed-loop propofol-remifentanil anesthesia guided by the WAV CNS index in the presence of an analgesic dose of ketamine. Thirty ASA I-II adults, 18-54 years, requiring general anesthesia for anterior cruciate ligament surgery were randomized to receive: full-dose [ketamine, 0.5 mg kg -1 initial bolus, 10 mcg kg -1 min -1 infusion] (recommended dose for postoperative pain management); half-dose [ketamine, 0.25 mg kg -1 bolus, 5 mcg kg -1 min -1 infusion]; or control [no ketamine]. After the ketamine bolus, patients received 1.0 mcg kg -1 remifentanil over 30 s, then 1.5 mg kg -1 propofol over 30 s, followed by manually-adjusted propofol-remifentanil anesthesia. The WAV CNS was > 60 for 7/9 patients in the full-dose group at 7 min after starting the propofol infusion. This was inconsistent with clinical observations of depth-of-hypnosis and significantly higher than control (median difference [MD] 17.0, 95% confidence interval [CI] 11.4-26.8). WAV CNS was median [interquartile range] 49.3 [42.2-62.6] in the half-dose group, and not different to control (MD 5.1, 95% CI - 4.9 to 17.9). During maintenance of anesthesia, the WAV CNS was higher in the full-dose group compared to control (MD 14.7, 95% CI 10.2-19.2) and in the half-dose group compared to control (MD 11.4, 95% CI 4.7-20.4). The full-dose of ketamine recommended for postoperative pain management had a significant effect on the WAV CNS . This effect should be considered when using the WAV CNS to guide propofol-remifentanil dosing.Trial Registration ClinicalTrails.gov No. NCT02908945.

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease., Competing Interests: Competing interests: The investigators in this study are academic researchers in the public sector. Investigators in Malawi receive salary compensation for their time invested carrying this study. Many investigators on this study are clinicians involved in caring for sick babies and have a deep, vested interest in generating new knowledge as well as ensuring safety and well-being of research participants., (Copyright: © 2020 Popescu CR et al.)