Predicting Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: Derivation Study.

Background: Delirium is prevalent in intensive care units (ICUs), often leading to adverse outcomes. Hypoactive delirium is particularly difficult to detect. Despite the development of new tools, the timely identification of hypoactive delirium remains clinically challenging due to its dynamic nature, lack of human resources, lack of reliable monitoring tools, and subtle clinical signs including hypovigilance.

Clinical Practice Guideline Recommendations on Mental Health in Trauma: A Systematic Review.

Importance: Many survivors of traumatic injuries are affected by mental disorders, which has recently led to the publication of clinical practice guidelines (CPGs). However, there is no comprehensive synthesis of guideline recommendations to inform clinicians on those that should be prioritized for implementation and thus promote adherence to them.

Objective: To identify guideline recommendations for the prevention and management of mental disorders in patients with traumatic injuries, appraise their quality, and synthesize the quality of evidence and the strength of included recommendations.

Discussions about goals of care in the emergency department: a qualitative study of emergency physicians' opinions using the normalization process theory.

Purpose: We explored emergency department (ED) physicians' opinions about leading goals of care discussions in their daily practice. We contextualized our findings within the current landscape of ED goals of care.

Methods: This qualitative study was based on the Normalization Process Theory.

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Objectives: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventions and compare its accuracy to that of logistic regression (LR).

Design: This retrospective observational study trained separate models using random-forest classifier (RFC), support vector machines (SVMs), Extreme Gradient Boosting (XGBoost), and multilayer perceptron (MLP) to predict three endpoints: eventual use of invasive ventilation, vasopressors, and RRT during hospitalization.

CAEP 2024 Academic Symposium: adaptive platform trials in emergency medicine in Canada.

Objective: The Canadian Association of Emergency Physicians 2024 Academic Symposium Panel on adaptive platform trials explored whether adaptive platform trials could be implemented in Canadian emergency departments (EDs). This panel aimed to propose and refine recommendations formulated by the results of a rapid review and responses from a panel of experts about conducting adaptive platform trials in EDs.

Methods: From November 2023 to May 2024, a rapid review was conducted on the existing logistical and ethical barriers and facilitators to structuring adaptive platform trials in emergency medicine.

Evidence-Informed Quality Indicators for Pediatric Trauma Care.

Importance: Despite the unique physiological characteristics and health care needs of pediatric trauma patients, there is a lack of quality indicators (QIs) based on pediatric-specific evidence to support quality improvement in this population.

Objective: To develop a consensus-based set of QIs for acute pediatric trauma care that considers evidence on effectiveness, safety, cost-effectiveness, equity, and caregiver perspectives and is applicable in pediatric and nonpediatric trauma centers.

Design, Setting, And Participants: A modified Research and Development (RAND)/University of California Los Angeles (UCLA) expert consensus study was conducted consisting of an online survey and a virtual workshop, led by an independent moderator.

Impact of a team-based versus individual clinician-focused training approach on primary healthcare professionals' intention to have serious illness conversations with patients: A theory-informed process evaluation embedded within a cluster randomized trial.

Background: Cluster randomized trials (cRTs) on the effectiveness of training programs face complex challenges when conducted in real-world settings. Process evaluations embedded within cRTs can help explain their results by exploring possible causal mechanisms impacting training effectiveness.

Objective: To conduct a process evaluation embedded within a cRT by comparing the impact of team-based vs.

Liberation from mechanical ventilation using Extubation Advisor Decision Support (LEADS): protocol for a multicentre pilot trial.

Introduction: Timely successful liberation from invasive ventilation has the potential to minimise critically ill patients' exposure to invasive ventilation, save costs and improve outcomes; yet no trials have evaluated strategies to better inform extubation decision-making. The Liberation from mechanical ventilation using Extubation Advisor (EA) Decision Support (LEADS) Pilot Trial will assess the feasibility of a trial of a novel extubation decision support tool on feasibility metrics. The primary feasibility outcome will reflect our ability to recruit the desired population.

Building a Collaborative Translational Research Platform: Identifying Barriers and Enablers From Basic Research to Primary Healthcare.

Introduction The capacity to translate basic research discoveries into clinical applications and to synthesize, disseminate, and integrate clinical research results into practice remains challenging. To help innovate the means of communicating and disseminating knowledge between actors across the research-to-practice continuum, this study aims to identify barriers and enablers in building and implementing a collaborative platform that will bring together all the actors involved. Methods The study was conducted based on a qualitative descriptive design and a deductive thematic analysis.

Managing Select Medical Emergencies During Long-Duration Space Missions.

Introduction: Space agencies and private industries worldwide are planning long-duration missions, which come with complex medical challenges. Crews must be prepared for medical emergencies due to longer mission durations, increased travel distance, communication delays, and higher levels of isolation. This study aimed to systematically review the existing literature and knowledge gaps that could be related to the management of medical emergencies during long-duration space missions.

Development and usability testing of a multifaceted intervention to reduce low-value injury care.

Background: Multifaceted interventions that address barriers and facilitators have been shown to be most effective for increasing the adoption of high-value care, but there is a knowledge gap on this type of intervention for the de-implementation of low-value care. Trauma is a high-risk setting for low-value care, such as unnecessary diagnostic imaging and the use of specialized resources. The aim of our study was to develop and assess the usability of a multifaceted intervention to reduce low-value injury care.

Impact of vitamin C on the reduction of opioid consumption for acute musculoskeletal pain: A double-blind randomized control pilot study.

Introduction: Recent evidence has shown that vitamin C has analgesic and opioid sparing properties in immediate postoperative context. However, this has never been studied for acute musculoskeletal (MSK) emergency department (ED) injuries. The aim of this pilot study is to evaluate the feasibility of conducting a randomized placebo-controlled study to determine the opioid sparing and analgesic effect of vitamin C compared to placebo, in acute MSK injured ED patients.

Community versus academic hospital community-acquired pneumonia patients: a nested cohort study.

Background: Most Canadians receive their care in community hospitals, yet most clinical research is conducted in academic hospitals. This study aims to compare patients with community acquired pneumonia (CAP) treated in academic and community hospitals with respect to their demographics, clinical characteristics, treatments and outcomes.

Methods: This nested observational cohort substudy of the Community Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) trial included 1,329 hospitalized adults with CAP recruited between March 1st, 2018 and September 31st, 2023 from 15 Canadian hospitals.

Accuracy of the Canadian COVID-19 Mortality Score (CCMS) to predict in-hospital mortality among vaccinated and unvaccinated patients infected with Omicron: a cohort study.

Objective: The objective is to externally validate and assess the opportunity to update the Canadian COVID-19 Mortality Score (CCMS) to predict in-hospital mortality among consecutive non-palliative COVID-19 patients infected with Omicron subvariants at a time when vaccinations were widespread.

Design: This observational study validated the CCMS in an external cohort at a time when Omicron variants were dominant. We assessed the potential to update the rule and improve its performance by recalibrating and adding vaccination status in a subset of patients from provinces with access to vaccination data and created the adjusted CCMS (CCMS).

Long-Term Effects of Individual-Focused and Team-Based Training on Health Professionals' Intention to Have Serious Illness Conversations: A Cluster Randomised Trial.

Unlabelled: We aimed to measure the sustainability of health professionals' intention to have serious illness conversations with patients using the Serious Illness Conversation Guide (SICG) after individual-focused training versus team-based training. In a cluster randomised trial, we trained healthcare professionals in 40 primary care clinics and measured their intention to hold serious illness conversations immediately (T1), after 1 year (T2) and after 2 years (T3). Primary care clinics ( = 40) were randomly assigned to individual-focused training (comparator) or team-based training (intervention).

Caregivers' burden of care during emergency department care transitions among older adults: a mixed methods cohort study.

Objective: Improving care transitions for older adults can reduce emergency department (ED) revisits, and the strain placed upon caregivers. We analyzed whether caregivers felt a change in burden following a care transition, and what may be improved to reduce it.

Methods: This mixed-methods observational study nested within LEARNING WISDOM included caregivers of older patients who experienced an ED care transition.

Nonmedical problems among older adults visiting the emergency department for low acuity conditions: A prospective multicentre cohort study.

Background: Data on the predictors of nonmedical problems (NMP) in older adults attending the emergency department (ED) for low acuity conditions is lacking and could help rapid identification of patients with NMP and integration of these needs into care planning.

Objectives: To determine the prevalence and predictors of NMP among older adults attending EDs for low acuity conditions.

Methods: Prospective cohort study in eight EDs (May-August 2021).

Prioritizing Protecting Oneself Over the COVID-19 Virus Versus Other Health and Social Needs Among Older Adults Living Alone: A Qualitative Study.

Background And Objectives: People aged 65 and older, deemed most "vulnerable" by public health, were targeted by the coronavirus disease 2019 protection measures, which sought to minimize physical contact and social activities. Older adults living alone were particularly affected by these measures. However, such measures meant to protect the older population may not have necessarily reflected older adults' individual prioritization choices.

Machine learning workflow for edge computed arrhythmia detection in exploration class missions.

Deep-space missions require preventative care methods based on predictive models for identifying in-space pathologies. Deploying such models requires flexible edge computing, which Open Neural Network Exchange (ONNX) formats enable by optimizing inference directly on wearable edge devices. This work demonstrates an innovative approach to point-of-care machine learning model pipelines by combining this capacity with an advanced self-optimizing training scheme to classify periods of Normal Sinus Rhythm (NSR), Atrial Fibrillation (AFIB), and Atrial Flutter (AFL).