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In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient's numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management. [ABSTRACT FROM AUTHOR]

Objective: To describe antibiotic treatment durations that pediatric infectious diseases (ID) and critical care clinicians usually recommend for bloodstream infections in critically ill children. Design: Anonymous, online practice survey using five common pediatric-based case scenarios of bloodstream infections. Setting: Pediatric intensive care units in Canada, Australia and New Zealand. Participants: Pediatric intensivists, nurse practitioners, ID physicians and pharmacists. Main outcome measures: Recommended treatment durations for common infectious syndromes associated with bloodstream infections and willingness to enrol patients into a trial to study treatment duration. Results: Among 136 survey respondents, most recommended at least 10 days antibiotics for bloodstream infections associated with: pneumonia (65%), skin/soft tissue (74%), urinary tract (64%) and intra-abdominal infections (drained: 90%; undrained: 99%). For central vascular catheter-associated infections without catheter removal, over 90% clinicians recommended at least 10 days antibiotics, except for infections caused by coagulase negative staphylococci (79%). Recommendations for at least 10 days antibiotics were less common with catheter removal. In multivariable linear regression analyses, lack of source control was significantly associated with longer treatment durations (+5.2 days [95% CI: 4.4–6.1 days] for intra-abdominal infections and +4.1 days [95% CI: 3.8–4.4 days] for central vascular catheter-associated infections). Most clinicians (73–95%, depending on the source of bloodstream infection) would be willing to enrol patients into a trial of shorter versus longer antibiotic treatment duration. Conclusions: The majority of clinicians currently recommend at least 10 days of antibiotics for most scenarios of bloodstream infections in critically ill children. There is practice heterogeneity in self-reported treatment duration recommendations among clinicians. Treatment durations were similar across different infectious syndromes. Under appropriate clinical conditions, most clinicians would be willing to enrol patients into a trial of shorter versus longer treatment for common syndromes associated with bloodstream infections. [ABSTRACT FROM AUTHOR]

Background: Bloodstream infections (BSIs) cause significant morbidity and mortality in critically ill children but treatment duration is understudied. We describe the durations of antimicrobial treatment that critically ill children receive and explore factors associated with treatment duration.Methods: We conducted a retrospective observational cohort study in six pediatric intensive care units (PICUs) across Canada. Associations between treatment duration and patient-, infection- and pathogen-related characteristics were explored using multivariable regression analyses.Results: Among 187 critically ill children with BSIs, the median duration of antimicrobial treatment was 15 (IQR 11-25) days. Median treatment durations were longer than two weeks for all subjects with known sources of infection: catheter-related 16 (IQR 11-24), respiratory 15 (IQR 11-26), intra-abdominal 20 (IQR 14-26), skin/soft tissue 17 (IQR 15-33), urinary 17 (IQR 15-35), central nervous system 33 (IQR 15-46) and other sources 29.5 (IQR 15-55) days. When sources of infection were unclear, the median duration was 13 (IQR 10-16) days. Treatment durations varied widely within and across PICUs. In multivariable linear regression, longer treatment durations were associated with severity of illness (+ 0.4 days longer [95% confidence interval (CI), 0.1 to 0.7, p = 0.007] per unit increase in PRISM-IV) and central nervous system infection (+ 17 days [95% CI, 6.7 to 27.4], p = 0.001). Age and pathogen type were not associated with treatment duration.Conclusions: Most critically ill children with BSIs received at least two weeks of antimicrobial treatment. Further study is needed to determine whether shorter duration therapy would be effective for selected critically ill children. [ABSTRACT FROM AUTHOR]

This article describes a new approach for quantitative evaluation of respiration in the pediatric intensive care unit (PICU). Video sequences of thorax movements are recorded by two depth cameras to cover the 3-D surface of the torso and its lateral sides. The breathing activity implies a frame-by-frame surface deformation, which can be described by the volume variation of reconstructed surfaces between the consecutive video frames. A quantitative evaluation of the breathing pattern is then performed through a subtraction technique, thereby detecting the volume variation between the subsequent frames. A high-fidelity simulation was performed in a realistic environment designed for critically ill children. The simulation was then followed by a real-world evaluation, involving two newborn babies (one female and one male) who required ventilator support. The breathing signal patterns resulting from our approach were compared to those measured by mechanical ventilation in terms of their waveforms, evaluating the most significant dynamic parameters: tidal volume, respiratory rate, and minute ventilation. Our experimental study showed a significant agreement between the proposed 3-D imaging system and the gold standard method in estimating respiratory waveforms and parameters. This article presents the following innovations. First, we suggest a 3-D imaging system specifically designed for PICUs based on a contactless design. Second, we propose an efficient positioning mechanism for the cameras, offering a very high spatial coverage of the thoracoabdominal zone and considering the PICU constraints. Finally, we propose an objective vision-based method to quantitatively measure respiration for spontaneous breathing patients in PICUs. [ABSTRACT FROM AUTHOR]

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change. Data sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%). Performance metrics of predictive models: Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75. Conclusion: This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels. [ABSTRACT FROM AUTHOR]

Background: We aimed (1) to describe the characteristics of patient-ventilator asynchrony in a population of critically ill children, (2) to describe the risk factors associated with patient-ventilator asynchrony, and (3) to evaluate the association between patient-ventilator asynchrony and ventilator-free days at day 28. Methods: In this single-center prospective study, consecutive children admitted to the PICU and mechanically ventilated for at least 24 h were included. Patient-ventilator asynchrony was analyzed by comparing the ventilator pressure curve and the electrical activity of the diaphragm (Edi) signal with (1) a manual analysis and (2) using a standardized fully automated method. Results: Fifty-two patients (median age 6 months) were included in the analysis. Eighteen patients had a very low ventilatory drive (i.e., peak Edi < 2 µV on average), which prevented the calculation of patient-ventilator asynchrony. Children spent 27% (interquartile 22-39%) of the time in conflict with the ventilator. Cycling-off errors and trigger delays contributed to most of this asynchronous time. The automatic algorithm provided a NeuroSync index of 45%, confirming the high prevalence of asynchrony. No association between the severity of asynchrony and ventilator-free days at day 28 or any other clinical secondary outcomes was observed, but the proportion of children with good synchrony was very low. Conclusion: Patient-ventilator interaction is poor in children supported by conventional ventilation, with a high frequency of depressed ventilatory drive and a large proportion of time spent in asynchrony. The clinical benefit of strategies to improve patient-ventilator interactions should be evaluated in pediatric critical care. [ABSTRACT FROM AUTHOR]

Background: The current COVID-19 pandemic has resulted in over 54,800,000 SARS-CoV-2 infections worldwide with a mortality rate of around 2.5%. As observed in other airborne viral infections such as influenza and SARS-CoV-1, healthcare workers are at high risk for infection when performing aerosol-generating medical procedures (AGMP). Additionally, the threats of a global shortage of standard personal protective equipment (PPE) prompted many healthcare workers to explore alternative protective enclosures, such as the "aerosol box" invented by a Taiwanese anesthetist. Our study includes the design process of a protective barrier enclosure and its subsequent clinical implementation in the management of critically ill adults and children infected with SARS-CoV-2.Methods and Results: The barrier enclosure was designed for use in our tertiary care facility and named "SplashGuard CG" (CG for Care Givers). The device has been adapted using a multi- and interdisciplinary approach, with collaboration between physicians, respiratory therapists, nurses, and biomechanical engineers. Computer-aided design and simulation sessions throughout the entire process facilitated the rapid and safe implementation of the SplashGuard CG in different settings (intensive care unit, emergency department, and the operating room) during AGMPs such as bag-valve-mask ventilation, nasopharyngeal suctioning, intubation and extubation, and noninvasive ventilation. Indications for use and anticipatory precautions were communicated to all healthcare workers using the SplashGuard CG. The entire process was completed within one month.Conclusion: The rapid design, development, and clinical implementation of a new barrier enclosure, the "SplashGuard CG," was feasible in this time of crisis thanks to close collaboration between medical and engineering teams and the use of recurring simulation sessions to test and improve the initial prototypes. Following this accelerated process, it is necessary to maintain team skills, monitor any undesirable effects, and evaluate and continuously improve this new device. [ABSTRACT FROM AUTHOR]

The article presents a study which evaluated the relationships among serum cytokine levels, innate immune responsiveness and mortality in a multicenter cohort of critically ill children with influenza infection. It found that a tumor necrosis factor-α response was highly predictive of death and longer duration of intensive care unit (ICU) stay. It concluded that high serum levels of cytokines can coexist with marked innate immune response in children with critical influenza.

Background & Aims: Hyperammonemia results from reduction of hepatocyte function or enzyme of urea cycle deficiency. Hyperammonemia contributes to cerebral edema that may lead to cerebral herniation. The threshold of toxicity of ammonemia is unknown. Methods: We conducted a retrospective observational study in our pediatric intensive care unit. All children who developed hyperammonemia from January 2000 to April 2009 were included. Clinical and laboratory data at admission, specific treatments implemented, and ammonemias the first 7days after inclusion were collected. The outcome assessed was 28day mortality. Risk of mortality was estimated by a logistic regression model. Results: Ninety patients with liver failure (63.3%) and primary or secondary urea cycle defect (23.3%) were included. Patients with urea cycle defects were more likely to receive ammonia scavengers than patients with liver failure (47.6% versus 3.5%). The 28day mortality rate was 31.1%. Risk of mortality increased according to the ammonemia within 48h: odds ratio 1.5, 1.9, 3.3, 2.4 for ammonemia above 100, 150, 200, and 300μmol/L, respectively. Peak ammonemia ⩾200μmol/L within the first 48h was an independent risk factor for mortality, with greater risk found in liver failure than in urea cycle defect. Conclusions: Our study identifies a threshold of exposure to ammonia (⩾200μmol/L) above which mortality increases significantly, especially in liver failure. Specific treatments of hyperammonemia are rarely used in liver failure when compared with urea cycle defect even though use of ammonia scavengers may help to decrease ammonemia. [ABSTRACT FROM AUTHOR]