Physician well-being is vital to delivering high-quality emergency care. A supported and healthy emergency medicine workforce leads to better patient outcomes, fewer medical errors, and greater job satisfaction and staff retention.[1,2] Emergency physicians (EPs) face unique pressures, including shift work, high patient volumes and acuities, overcrowding, and systemic inefficiencies that escalate their risk of burnout. As a result, EPs have reported the highest rates of burnout among physician specialties.[1,3] Over the past decade, multiple attempts have been made to promote EP well-being. However, early initiatives were oriented around individual “wellness”, such as exercise, diet, sleep, yoga, and finding “work-life balance”. Recently, there has been evolving recognition of the effects of a number of aspects of burnout that are outside the control of individual EPs, including administrative duties superseding clinical duties, diminishing resources and support, overcrowding and boarding, adverse working conditions, and medico-legal challenges. These factors progressively constrain the ability and capacity of EPs to do the work that they are trained and entrusted to do - rapidly and effectively evaluate, treat, and dispose of acutely ill patients. This sense of “moral injury” has contributed to growing dissatisfaction and premature departure from emergency medicine through reduced hours, transitions to other fields, early retirement, and, tragically, suicide.[4,5]

BACKGROUND: To provide a comprehensive analysis of the landscape of artificial intelligence (AI) applications in cardiac arrest (CA). METHODS: Comprehensive searches were conducted in PubMed, the Cochrane Library, Web of Science, and EMBASE from database inception through 10 June 2025. Studies that applied AI in both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) populations across the following domains were included: prediction of cardiac arrest occurrence, prognostication of CA outcomes, applications of large language models (LLMs), and evaluation of cardiopulmonary resuscitation (CPR) and other AI-driven interventions related to CA. RESULTS: The scoping review included 114 studies, encompassing data from 9,574,462 patients in total. AI was most commonly applied to the prediction of CA (overall, n=40; IHCA, n=30; OHCA, n=4; and both, n=6), CPR-related decision support during CA (n=16), and post-arrest prognosis and rehabilitation outcomes (overall, n=38; OHCA, n=21; IHCA, n=3; and both, n=14). Additional application areas included LLM-based applications (n=8), emergency call handling (n=4), wearable device-based detection (n=3), heart rhythm identification (n=2), education (n=2), and extracorporeal cardiopulmonary resuscitation (ECPR) candidate identification (n=1). Across all application scenarios, the highest area under the receiver operating characteristic curve (AUROC) value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron (MLP) model, whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 using extreme gradient boosting (XGBoost) or random forest (RF) models. For CPR-related decision support during CA, the highest AUROC achieved was 0.990 with a convolutional neural network (CNN) model. In prognostic prediction, the optimal AUROC for IHCA patients was 0.960 using XGBoost, while for OHCA patients it reached 0.976 using an MLP model. CONCLUSION: This review shows that AI is most commonly used for the prediction of CA and CPR-related support, as well as post-arrest and rehabilitation outcomes. Future research directions include drug discovery, post-resuscitation management, neurorehabilitation, and clinical trial innovation. Further studies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validate their effectiveness across diverse patient populations. Overall, AI has significant potential to improve clinical practice, and its role in CA application is increasingly important.

BACKGROUND: Maxillofacial trauma represents a significant challenge in emergency medicine, requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation of function and aesthetics. The complex anatomy of this region, with its proximity to critical structures, demands a thorough understanding of assessment and management principles. This narrative review aims to provide evidence-based guidelines for emergency physicians managing maxillofacial trauma, with particular emphasis on early recognition of critical injuries, airway management strategies, and special population considerations. METHODS: A narrative review was conducted via a comprehensive literature search of the PubMed and Scopus databases, which focused on maxillofacial trauma management in emergency settings. Articles were selected based on relevance to clinical practice, methodological quality, and current management guidelines. The review synthesized evidence from multiple study types, including original research, systematic reviews, and clinical practice guidelines, to provide practical guidance for emergency physicians. RESULTS: Initial assessment following Advanced Trauma Life Support (ATLS) principles is crucial, with airway management being a primary concern due to the risk of dynamic obstruction. Critical time-sensitive emergencies include orbital compartment syndrome, trapdoor fractures (in pediatric patients), and facial nerve injuries. Computed tomography (CT) imaging remains the gold standard for diagnosis. Special considerations are required for pediatric patients, who present unique anatomical challenges and injury patterns, and for elderly patients, who often have complex medical comorbidities and increased complication risks. Management strategies range from conservative treatment to urgent surgical intervention, with decisions based on the injury pattern and associated complications. CONCLUSION: Emergency physicians must maintain a structured yet flexible approach to maxillofacial trauma, focusing on early recognition of critical injuries, appropriate airway management, and timely specialist consultation. Understanding injury patterns and their potential complications allows for effective risk stratification and treatment planning, ultimately improving patient outcomes.

BACKGROUND Sepsis is a prevalent and severe condition, with microcirculation disruptions playing a crucial role in its progression. Endothelial cell (EC) injury is the primary factor behind microcirculatory issues. This review is to outline the pathomechanism, organ heterogeneity, biomarkers, and therapeutic implications of endothelial dysfunction in sepsis, offering references and insights for the clinical management of sepsis. METHODS: A systematic search of Web of Science and PubMed from inception to June 10, 2025, limited to English publications, was conducted. Two reviewers independently identified studies on EC injury in patients with septic microcirculatory dysfunction. Duplicate articles based on multiple search criteria were excluded. RESULTS: Fifty-nine articles, including cell, animal, and clinical studies, were included. These studies reported the effects of EC injury on the microcirculation in sepsis, including changes in vascular permeability, coagulation dysfunction, vasomotor regulation, and inflammatory responses. These pathways interact and ultimately lead to septic microcirculation disorders. CONCLUSION: Sepsis-induced endothelial dysfunction involves various interconnected mechanisms, which collectively compromise ECs and impede microcirculatory perfusion. Future research should enhance current understanding of endothelial injury mechanisms, develop synergistic multi-target strategies to disrupt this cycle, and facilitate the clinical application of endothelial markers for early intervention and dynamic assessment.

BACKGROUND Acute pain is a sudden experience secondary to injuries and varies in perception among individuals. In trauma patients, it can negatively affect respiratory function, immune response, and wound healing, making it a significant public health concern. This study is to determine the prevalence and factors associated with acute pain among emergency trauma patients. METHODS: A multicenter cross-sectional study was conducted. Data were collected via interviewer-administered questionnaires and patient chart review. The data were analyzed via the statistical package for social science version 25. Bivariable and multivariable logistic regression analyses were used. Variables with a P-value <0.05 were considered statistically significant. RESULTS: A total of 397 patients were included in the study, for a response rate of 96.8%. The prevalence of pain during admission was 91.9% (95% confidence intervals [95% CIs]: 88.8%-94.4%). Blunt trauma (adjusted odds ratio [aOR]=2.82; 95% CI: 1.23-6.45), analgesia before admission to the emergency department (aOR=2.71; 95% CI: 1.16-6.36), documentation of pain severity in the chart (aOR=2.71; 95% CI: 1.16-6.36), analgesia provided within two hours after admission (aOR=7.60; 95% CI: 2.79-20.68), use of non-pharmacological pain management methods (aOR=3.09; 95% CI: 1.35-7.08) and availability of analgesia (aOR=3.95; 95% CI: 1.36-11.43) were associated with acute pain experience. CONCLUSION: The prevalence of acute pain among emergency trauma patients was high in the study area. Analgesia should be administered prior to admission, and non-pharmacological pain management should be implemented. Moreover, training on pain assessment and management should be provided for healthcare providers in the emergency department.

BACKGROUND This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome (AAS) in the emergency department (ED) and to derive a simplified version suitable for rapid clinical application. METHODS: In this multi-center retrospective cohort study, AAS patient data from three hospitals were analyzed. The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region, with Peking University Third Hospital data serving as the external test set. Four machine learning algorithms—logistic regression (LR), multilayer perceptron (MLP), Gaussian naive Bayes (GNB), and random forest (RF)—were used to develop predictive models based on 34 early-accessible clinical variables. A simplified model was then derived based on five key variables (Stanford type, pericardial effusion, asymmetric peripheral arterial pulsation, decreased bowel sounds, and dyspnea) via Least Absolute Shrinkage and Selection Operator (LASSO) regression to improve ED applicability. RESULTS: A total of 929 patients were included in the modeling cohort, and 210 were included in the external test set. Four machine learning models based on 34 clinical variables were developed, achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85, respectively. The simplified model incorporating five key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78, respectively. Both models showed robust calibration and predictive stability across datasets. CONCLUSION: Both kinds of models were built based on machine learning tools, and proved to have certain prediction performance and extrapolation.

BACKGROUND: Serum osmolality is a prognostic indicator in critically ill patients. This study aimed to evaluate the association between high osmolality and 28-day mortality in patients with cardiac arrest (CA) admitted to the intensive care unit (ICU). METHODS: Baseline data of adult patients with CA who were admitted to the ICU from 2008 to 2019 were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV. Patients were divided into survivor and non-survivor groups according to the 28-day prognosis. Serum concentrations of sodium, potassium, glucose, and urea nitrogen on the first day of ICU admission were used to determine serum osmolarity. The primary endpoint of this study was 28-day all-cause mortality. Propensity score matching (PSM) analysis was performed to reduce bias between the survivor and non-survivor groups. RESULTS: Among the 798 included CA patients, the high osmolarity on the first day of ICU admission remained significantly associated with increased 28-day mortality (62.0% vs. 35.5%, P<0.001) and reduced cumulative survival (log-rank P<0.05) after PSM. Cox regression identified the high osmolarity on the first day of ICU admission as an independent predictor. High serum osmolarity on the first day of ICU admission effectively predicted 1-, 3-, 7-, and 28-day all-cause mortality, with the strongest predictive performance for 1-day mortality both before and after PSM (all P<0.05). CONCLUSION: In this study, elevated serum osmolarity on the first day of ICU admission was independently associated with increased 28-day mortality in CA patients and could serve as a prognostic marker.

BACKGROUND Sepsis is a highly heterogeneous organ dysfunction syndrome. There is limited evidence regarding phenotypes and clinical outcomes in sepsis patients with initial normal lactate levels. We sought to identify the lactate-based clinical phenotypes and outcomes of sepsis patients. METHODS: The Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU databases were used to conduct a retrospective cohort study. Adult sepsis patients were included. Lactate was measured via blood gas, and the same assay type was used across both databases. Serial lactate measurements were analyzed via a two-point classification system based on the highest values recorded during two consecutive 24-hour periods following ICU admission. The first measurement window (T1) comprised the initial 24 h post-admission, whereas the second window (T2) covered 24-48 h post-admission. The lactate difference was defined as the numerical change between the highest lactate level at T2 and the highest level at T1. The time interval between these two measurements was fixed, with T2 commencing immediately after T1, together encompassing the first 48 h post-ICU admission. A normal lactate level was defined as ≤2 mmol/L, and an elevated level was defined as >2 mmol/L. Sepsis patients were stratified into four trajectory phenotypes: (1) normal‒normal (N‒N); (2) normal-elevated (N‒E); (3) elevated-normal (E‒N); and (4) elevated-elevated (E‒E). The primary outcome was in-hospital mortality. RESULTS: This study enrolled 6,926 sepsis patients. The clinical phenotypes of the sepsis patients were as follows: N‒N (24.4%), N‒E (3.8%), E‒N (36.4%), and E‒E (35.3%). The in-hospital mortality rates of sepsis patients with the four phenotypes from the MIMIC-IV and eICU databases were as follows (N‒N: 18.9% vs. 17.6%, P=0.66; N‒E: 35.3% vs. 29.2%, P=0.45; E‒N: 16.6% vs. 14.2%, P=0.14; E‒E: 43.6% vs. 37.8%, P=0.01). After adjusting for age, sex, Sequential Organ Failure Assessment (SOFA) score, vasopressor therapy, and infection sites, the N‒E phenotype was associated with a higher risk of in-hospital mortality (odds ratio [OR] 1.44; 95% confidence intervals [95% CI] 1.11-1.86; P=0.006; adjusted OR 1.61; 95% CI 1.23-2.11; P<0.001). The E‒N phenotype was associated with the most favorable outcomes for in-hospital mortality in the multivariable analysis (adjusted OR 0.41; 95% CI 0.36-0.46; P<0.001). The E‒E phenotype was associated with the highest risk of in-hospital mortality in the overall cohort (adjusted OR 3.00; 95% CI2.67-3.37; P<0.001). CONCLUSION: In sepsis patients with normal initial lactate levels, serial lactate measurements could be valuable for prognostic assessment.

BACKGROUND: This study is to evaluate clearance effects of hemoperfusion (HP), continuous renal replacement therapy (CRRT), and plasma exchange (PE) for chlorfenapyr and its metabolite tralopyril in patients with acute poisoning. METHODS: This retrospective study included 18 patients with acute oral chlorfenapyr poisoning treated at our department between January 2022 and January 2024. All patients received conventional therapies combined with blood purification, including HP, CRRT, and PE. HP was performed three sessions within the first 24 h, followed by CRRT and PE. Serial blood samples were collected to measure plasma concentrations of chlorfenapyr and tralopyril using gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). The toxin-clearance effects were assessed using a linear mixed-effects (LME) model. RESULTS: The hourly decline rate of the plasma chlorfenapyr concentration (median [IQR]) was 8.83% (1.79%) for HP, 4.12% (1.26%) for CRRT, and 6.85% (1.44%) for PE. LME analysis showed higher decline rate in the plasma concentration with HP (β=5.00; P<0.001) and PE (β=2.15; P=0.003) compared to CRRT. For tralopyril, the hourly decline rates were 3.04% (0.62%) for HP, 1.82% (0.48%) for CRRT, and 3.01% (0.37%) for PE. LME analysis showed that the clearance effects of HP (β=0.027; P<0.001) and PE (β=0.022; P=0.001) were superior to CRRT. Pre-treatment toxin levels and the interval from hospital admission to blood purification showed no significant interaction with clearance outcomes. CONCLUSION: In our study, HP was associated with a higher decline rate in plasma chlorfenapyr concentration compared to CRRT and PE, supporting HP as a preferred early intervention. However, all three methods showed limited efficacy in reducing tralopyril levels. Further research into the toxicokinetics and mechanisms of chlorfenapyr is warranted to optimize purification strategies.

BACKGROUND Individualized positive end-expiratory pressure (PEEP) titration is a crucial technique in mechanical ventilation therapy for acute respiratory distress syndrome (ARDS) patients with intra-abdominal hypertension (IAH). This study aimed to evaluate the effectiveness of electrical impedance tomography (EIT)-guided PEEP titration in this population. METHODS: This prospective study enrolled 36 ARDS patients, including 22 patients with IAH and 14 without IAH. All the patients underwent EIT-guided PEEP titration at the intersection point between alveolar overdistension and collapse during a decremental PEEP trial. The changes in pulmonary ventilation distribution, respiratory mechanics and hemodynamics during the titration process were observed. RESULTS: After EIT-guided PEEP titration was performed, the PEEP, peak inspiratory pressure and plateau pressure increased significantly (P<0.05). Furthermore, no significant differences were observed in respiratory system compliance, tidal volume, driving pressure, or the 4*DP+RR index between the two groups (P>0.05). The mechanical power increased in the non-IAH (NIAH) group after PEEP titration (P<0.05). Ventilation in gravity-dependent lung regions significantly increased (P<0.05), and the oxygenation index (PaO2/FiO2) improved significantly (P<0.05) in both groups. However, blood pressure, heart rate, respiratory rate, central venous pressure, and lactate levels did not significantly change. In the IAH group, the PaO2/FiO2 ratio improved less than that in the NIAH group did (P<0.05). CONCLUSION: In our study, individualized PEEP titration guided by EIT improved oxygenation in ARDS patients with concomitant IAH without significantly affecting hemodynamics. The presence of IAH may limit the improvement of oxygenation during EIT-guided PEEP titration.