Volatile gas scavenging in the paediatric intensive care unit: Occupational health and safety assessment.

Introduction: The use of volatile anesthetic agents in the paediatric intensive care unit (PICU) is experiencing increased interest since the availability of the miniature vapourizing device. However, the effectiveness of scavenging systems in the presence of humidifiers in the ventilator circuit is unknown. Approach Methods: We performed a bench study to evaluate the effectiveness of the Deltasorb® scavenging system in the presence of isoflurane and active humidity by simulating both infant and child ventilator test settings. A total of four ventilators were set to ventilate test lungs, all with active humidity and a Deltasorb scavenging canister collecting exhaled ventilation gas. Two ventilators also had isoflurane delivered using the Anesthesia Conserving Device- small (ACD®-S) on the inspiratory limb (also called alternative ventilator configuration). We performed instantaneous measurements of isoflurane and continuous sampling with passive badges to measure average environmental exposure over a test period of 6.5 hours. Scavenging canisters were returned to the company, where desorption analysis showed the volume of water and isoflurane captured in each canister. Findings: Both instantaneous point sampling and diffusive sampling results were below the occupational exposure limit confirming safety. The canisters collected both isoflurane and a portion of the water vapour delivered; the percentage of captured water and isoflurane collected in infants was higher than the child ventilator test settings. Practice implications Conclusion: The tested scavenging configuration was effective in maintaining a safe working environment with active humidity and inspiratory limb (alternative) ventilator configuration of the the miniature vapourizing device.

Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus.

BACKGROUND: The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG). METHODS: EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel. RESULTS: Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126 min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126 min), whereas qEEG novices reported higher burden (6066 vs. 3126 min). CONCLUSIONS: Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.

Inhaled Volatiles for Status Asthmaticus, Epilepsy, and Difficult Sedation in Adult ICU and PICU: A Systematic Review.

OBJECTIVES: Inhaled volatile anesthetics support management of status asthmaticus (SA), status epilepticus (SE), and difficult sedation (DS). This study aimed to evaluate the effectiveness, safety, and feasibility of using inhaled anesthetics for SA, SE, and DS in adult ICU and PICU patients. DATA SOURCES: MEDLINE, Cochrane Central Register of Controlled Trials, and Embase. STUDY SELECTION: Primary literature search that reported the use of inhaled anesthetics in ventilated patients with SA, SE, and DS from 1970 to 2021. DATA EXTRACTION: Study data points were extracted by two authors independently. Quality assessment was performed using the Joanna Briggs Institute appraisal tool for case studies/series, Newcastle criteria for cohort/case-control studies, and risk-of-bias framework for clinical trials. DATA SYNTHESIS: Primary outcome was volatile efficacy in improving predefined clinical or physiologic endpoints. Secondary outcomes were adverse events and delivery logistics. From 4281 screened studies, the number of included studies/patients across diagnoses and patient groups were: SA (adult: 38/121, pediatric: 28/142), SE (adult: 18/37, pediatric: 5/10), and DS (adult: 21/355, pediatric: 10/90). Quality of evidence was low, consisting mainly of case reports and series. Clinical and physiologic improvement was seen within 1-2 hours of initiating volatiles, with variable efficacy across diagnoses and patient groups: SA (adult: 89-95%, pediatric: 80-97%), SE (adults: 54-100%, pediatric: 60-100%), and DS (adults: 60-90%, pediatric: 62-90%). Most common adverse events were cardiovascular, that is, hypotension and arrhythmias. Inhaled sedatives were commonly delivered using anesthesia machines for SA/SE and miniature vaporizers for DS. Few (10%) of studies reported required non-ICU personnel, and only 16% had ICU volatile delivery protocol. CONCLUSIONS: Volatile anesthetics may provide effective treatment in patients with SA, SE, and DS scenarios but the quality of evidence is low. Higher-quality powered prospective studies of the efficacy and safety of using volatile anesthetics to manage SA, SE, and DS patients are required. Education regarding inhaled anesthetics and the protocolization of their use is needed.

The Spectrum of Quantitative EEG Utilization Across North America: A Cross-Sectional Survey.

BACKGROUND: Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use. METHODS: An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education. RESULTS: Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula. CONCLUSIONS: ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers.

Pediatric severe traumatic brain injury mortality prediction determined with machine learning-based modeling.

INTRODUCTION: Severe traumatic brain injury (sTBI) is a leading cause of mortality in children. As clinical prognostication is important in guiding optimal care and decision making, our goal was to create a highly discriminative sTBI outcome prediction model for mortality. METHODS: Machine learning and advanced analytics were applied to the patient admission variables obtained from a comprehensive pediatric sTBI database. Demographic and clinical data, head CT imaging abnormalities and blood biochemical data from 196 children and adolescents admitted to a tertiary pediatric intensive care unit (PICU) with sTBI were integrated using feature ranking by way of a forest of randomized decision trees, and a model was generated from a reduced number of admission variables with maximal ability to discriminate outcome. RESULTS: In total, 36 admission variables were analyzed using feature ranking with variable weighting to determine their predictive importance for mortality following sTBI. Reduction analysis utilizing Borata feature selection resulted in a parsimonious six-variable model with a mortality classification accuracy of 82%. The final admission variables that predicted mortality were: partial thromboplastin time (22%); motor Glasgow Coma Scale (21%); serum glucose (16%); fixed pupil(s) (16%); platelet count (13%) and creatinine (12%). Using only these six admission variables, a t-distributed stochastic nearest neighbor embedding algorithm plot demonstrated visual separation of sTBI patients that lived or died, with high mortality predictive ability of this model on the validation dataset (AUC = 0.90) which was confirmed with a conventional area-under-the-curve statistical approach on the total dataset (AUC = 0.91; P < 0.001). CONCLUSIONS: Machine learning-based modeling identified the most clinically important prognostic factors resulting in a pragmatic, high performing prognostic tool for pediatric sTBI with excellent discriminative ability to predict mortality risk with 82% classification accuracy (AUC = 0.90). After external multicenter validation, our prognostic model might help to guide treatment decisions, aggressiveness of therapy and prepare family members and caregivers for timely end-of-life discussions and decision making. LEVEL OF EVIDENCE: III; Prognostic.

A Survey of Neuromonitoring Practices in North American Pediatric Intensive Care Units.

BACKGROUND: Neuromonitoring is the use of continuous measures of brain physiology to detect clinically important events in real-time. Neuromonitoring devices can be invasive or non-invasive and are typically used on patients with acute brain injury or at high risk for brain injury. The goal of this study was to characterize neuromonitoring infrastructure and practices in North American pediatric intensive care units (PICUs). METHODS: An electronic, web-based survey was distributed to 70 North American institutions participating in the Pediatric Neurocritical Care Research Group. Questions related to the clinical use of neuromonitoring devices, integrative multimodality neuromonitoring capabilities, and neuromonitoring infrastructure were included. Survey results were presented using descriptive statistics. RESULTS: The survey was completed by faculty at 74% (52 of 70) of institutions. All 52 institutions measure intracranial pressure and have electroencephalography capability, whereas 87% (45 of 52) use near-infrared spectroscopy and 40% (21/52) use transcranial Doppler. Individual patient monitoring decisions were driven by institutional protocols and collaboration between critical care, neurology, and neurosurgery attendings. Reported device utilization varied by brain injury etiology. Only 15% (eight of 52) of institutions utilized a multimodality neuromonitoring platform to integrate and synchronize data from multiple devices. A database of neuromonitoring patients was maintained at 35% (18 of 52) of institutions. Funding for neuromonitoring programs was variable with contributions from hospitals (19%, 10 of 52), private donations (12%, six of 52), and research funds (12%, six of 52), although 73% (40 of 52) have no dedicated funds. CONCLUSIONS: Neuromonitoring indications, devices, and infrastructure vary by institution in North American pediatric critical care units. Noninvasive modalities were utilized more liberally, although not uniformly, than invasive monitoring. Further studies are needed to standardize the acquisition, interpretation, and reporting of clinical neuromonitoring data, and to determine whether neuromonitoring systems impact neurological outcomes.