Archives of Academic Emergency Medicine最新文献

Introduction: Effective information transfer between healthcare providers is essential for patient safety. This study aimed to evaluate the impact of ISBAR (Identify, Situation, Background, Assessment, Recommendation) framework on the quality of clinical handovers in emergency department (ED).

Methods: This prospective, pre- and post-intervention study was conducted at Hazrat Ali Asghar Pediatric Hospital in Tehran, Iran, from May to September 2023. A total of 428 clinical handovers were recorded (214 pre-intervention and 214 post-intervention) following a 90-minute training session and the introduction of a standardized ISBAR checklist. Handover quality was measured using the completeness of a 16-item ISBAR checklist. Data analysis employed descriptive statistics, the Mann-Whitney U test, and Chi-square tests.

Results: Implementation of the ISBAR protocol significantly improved the overall quality of information conveyed during handovers. Total handover scores increased from a mean rank of 127.55 pre-intervention to 301.45 post-intervention (P < 0.001). All five ISBAR domains showed significant enhancements; Identify (from 145.41 to 283.59, P=0.001), Situation (from 129.64 to 299.36, P=0.001), Background (from 136.40 to 292.60, P=0.001), Assessment (from 156.00 to 273.00, P< 0.001), and Recommendations (from 198.14 to 230.86, P=0.03). In addition, the completeness of individual items such as patient diagnosis, admission date, and vital signs improved markedly.

Conclusions: Adopting a standardized ISBAR handover protocol in a high-stakes pediatric environment ED significantly enhances the accuracy and completeness of patient handovers, thereby reducing the potential for errors and strengthening patient safety.

Introduction: The stroke scale for the mid-level personnel (SML) was designed for emergency medical services personnel to predict acute ischemic stroke due to large vessel occlusion (LVO) in both prehospital and in-hospital settings. This study aimed to validate and determine the appropriate cut point of the SML score in this regard.

Methods: This single-centered, prospective validation study to assess a novel LVO triage tool was performed in a tertiary care hospital in Bangkok. Patients presenting within 24 hours of onset of acute stroke were included in the study. The scale is designed for mid-level providers and emergency medical services (EMS) personnel including paramedics, emergency medical technicians (EMTs) and emergency department (ED) nurses. LVO was confirmed by brain and neck computed tomography angiography (CTA). Area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LRs), and correctly classified instances (CCI) were calculated. Youden's index was used to determine an appropriate cut point of the SML score for LVO prediction.

Results: 200 cases with the median age of 64.0 (56.5-73.0) years were included (53.5% female). 83 (41.5%) cases were affiliated to the LVO and 117 (58.5%) to the non-LVO group. The median SML scores for non-LVO and LVO stroke patients were 3 (2 - 3) and 6 (5 - 7), respectively (p < 0.001). The most common presentations in both groups were facial palsy, arm weakness and speech impairment or dysarthria. There was significantly higher prevalence of neglect (8 (6.8%) vs. 5 (4.3%); p < 0.001) and eye deviation (39 (47%) vs. 29 (35%); p < 0.001) in the LVO stroke group than in the non-LVO group. LVO patients scored higher in all categories when compared to non-LVO cases. SML scores of 4 and 5 had the highest Youden's index of 0.82 and 0.67, respectively. SML score of 4 yielded the highest correctly classified instances (CCI) of 90% with sensitivity and specificity of 96.4% (95% confidence interval (CI): 89.9-99.3%) and 85.3% (95% CI: 77.6-91.2), respectively. SML score of 4 also achieved the lowest negative LR of 0.04 and an odds ratio of 157 (95% CI: 46.7-521). The AUC of SML in cutoff point of 4 was 0.901 (95%CI: 0.853 - 0.949).

Conclusions: SML score may be helpful for mid-level medical providers and also EMS personnel in detecting LVOs since prehospital phase. According to the results, we recommend a cut point SML score ≥ 4 for enhanced sensitivity and NPV.

Introduction: Accurate prediction of mortality following myocardial infarction (MI) is critical for timely identification of high-risk patients and optimization of interventions. Conventional statistical models are commonly used; however, advanced machine learning (ML) methods are being increasingly recognized. This meta-analysis aimed to systematically evaluate and compare the predictive performances of various ML models.

Methods: A systematic search of the Medline (via PubMed), Embase, Scopus, and Web of Science databases was conducted up to January 9, 2025. A total of 14933 articles were identified, of which 330 underwent a full-text review and 69 met the inclusion criteria. The meta-analysis was conducted using a bivariate random-effects model in the 'midas' package of STATA 14. Subgroup analyses were conducted based on the follow-up duration and selected clinical features. The risk of bias was assessed using the QUAPAS. Publication bias and evidence certainty were assessed using Deeks' funnel plots and GRADE framework, respectively.

Results: Gradient Boosting Machines (GBM), Single Decision Tree Models, and Random Forest models yielded similarly high predictive accuracies. Advanced GBMs, particularly XGBoost (AUC = 0.90, 95% CI: 0.87-0.92; sensitivity = 0.78, 95% CI: 0.74-0.82; specificity = 0.87, 95% CI: 0.83-0.89), showed the highest evidence certainty due to precision and minimal publication bias. Across advanced GBMs, adding echocardiographic parameters increased the sensitivity from 0.77 to 0.83 and specificity from 0.85 to 0.90, indicating a clinically meaningful yet resource-dependent gain in discrimination.

Conclusions: Advanced Gradient Boosting Machines, particularly XGBoost, currently provide the most reliable mortality predictions in patients with MI. Future research should emphasize external validation, transparent reporting of feature selection, detailed data preprocessing, and dedicated studies on populations with NSTEMI.

Introduction: Chest Compression Synchronized Ventilation (CCSV) is a novel approach aimed at optimizing gas exchange and hemodynamics during cardiopulmonary resuscitation (CPR). However, its clinical value, safety profile and implementation barriers remain unclear. This study aimed to systematically synthesize existing evidence on the use of CCSV during cardiac arrest in animals and humans.

Methods: We conducted a scoping review and systematically searched five databases (Medline, Embase, CENTRAL, Scopus, Web of Science) up to May 2025. Studies investigating CCSV or mechanistically related ventilation strategies during cardiac arrest were included regardless of study design, language or publication date. Data were charted for study characteristics, outcomes and adverse events.

Results: Thirty-two studies published between 1980 and 2025 were included. Most were animal studies (n=19), primarily conducted in pigs, with limited human data (n=10). CCSV showed positive effects on arterial oxygenation, carbon dioxide clearance, and hemodynamic parameters as well as cerebral oxygenation compared to conventional ventilation modes. Adverse events such as pneumothorax and lung injury were inconsistently reported.

Conclusions: Available data on CCSV suggests potential physiological benefits during CPR, particularly in experimental settings. Human data remain scarce, and larger, prospective human trials are essential to evaluate clinical effectiveness, guide implementation, and assess risks compared to conventional ventilation strategies.

Introduction: One of the preventable contributors to trauma mortality is hemorrhagic shock, which requires early recognition and immediate intervention. In this retrospective analysis, we aimed to develop and optimize machine learning (ML) algorithms to predict the need for packed red blood cell (PRBC) transfusion within 24 hours of injury in multiple trauma patients.

Methods: This retrospective longitudinal study analyzed consecutive multiple trauma patients admitted to the emergency department. The outcome was transfusion of at least one unit of PRBC within the first 24 hours of traumatic injury. SHAP analysis was employed for feature selection, and the five key predictors were identified and entered in the models: Glasgow Coma Scale (GCS), hemoglobin (Hb), pulse rate (PR), systolic blood pressure (SBP), and pulse pressure. The dataset was split 80:20 for training/testing, and multiple machine learning algorithms were evaluated based on area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results: The study cohort consisted of 908 patients, with a median age of 34 years. PRBC transfusions were more common in older adults with lower GCS scores, higher PR, lower SBP, lower pulse pressure, and lower Hb levels on admission. Among the machine learning models, Random Forest performed best (AUC: 0.997, sensitivity: 0.938, specificity: 0.994), followed by K-Nearest Neighbors and Logistic Regression, both of which showed perfect specificity but lower sensitivity.

Conclusion: Random Forest outperformed other ML algorithms, achieving high discriminative ability, sensitivity, and specificity. PR, GCS, Hb, SBP, and pulse pressure were the most influential predictors of the need for early transfusion. Despite promising results, further multicenter validation studies are needed to confirm the real-world applicability of these models.

A study by Le Xuan et al. suggested that the lactate/albumin ratio (LAR) may aid in predicting sepsis-associated acute kidney injury. However, overlapping receiver operating characteristic (ROC) curve confidence intervals, creatinine-only acute kidney injury (AKI) definitions, and single-point biomarker assessments limit interpretation. Given its single-centre retrospective design, broader validation with dynamic biomarkers and kidney-specific comparators is needed before the LAR can be integrated into sepsis risk stratification.

Introduction: Diabetic ketoacidosis (DKA) is one of the complications of diabetes mellitus that requires rapid and accurate diagnosis. End-tidal carbon dioxide (EtCO₂) has been used for diagnosing DKA, however, there is uncertainty about the predicting value of this tool. In the current systematic review and meta-analysis, we purposed to assess the predictive value of EtCO₂ in diagnosing DKA.

Methods: We conducted a comprehensive search in PubMed, Scopus, and Web of Science for relevant studies and after screening based on the inclusion criteria, we extracted data. DKA diagnosis in the included studies was based on a composite clinical reference standard, including arterial blood gas (ABG) analysis and ketone testing. We used the Joanna Briggs Institute (JBI) checklist for diagnostic test accuracy studies for quality appraisal. Meta-analysis was performed based on the methods of the Cochrane DTA Handbook using the MetaDTA: Diagnostic Test Accuracy Meta-Analysis v2.1.3.

Results: A total of 13 studies were included in the systematic review, eight of which were proceeding to meta-analysis. The pooled sensitivity and specificity of EtCO₂ for diagnosing DKA were 0.96 (95% confidence interval (CI): 0.85-0.93) and 0.88 (95% CI: 0.79-0.93), respectively. The pooled diagnostic odds ratio (DOR) was 211.07 (95% CI: 38.3- 1162.1). The positive and negative likelihood ratios were 8.27 (95% CI: 4.6-14.7) and 0.03 (95% CI: 0.009-0.18), respectively. The results of the quality appraisal of include studies indicated moderate to low risk of bias.

Conclusions: The findings of this systematic review and meta-analysis show the high sensitivity and specificity of EtCO₂ in diagnosing DKA, which indicates its potential as a reliable diagnostic tool in emergency settings. However, the overall quality of the included studies, which were assessed to have medium to high risk of bias, should be considered when using EtCO₂ in clinical practice. Further high-quality research is needed to confirm the diagnostic value of EtCO₂ in emergency settings.

Introduction: Amiodarone has been reported to be associated with QTc interval prolongation and Torsades de pointes (TdP). This study aimed to assess the incidence and identify the risk factors of QTc prolongation and TdP associated with intravenous amiodarone therapy in patients diagnosed with atrial fibrillation (AF).

Methods: A retrospective cohort study was conducted using electronic health records of Buddhachinaraj Hospital, a tertiary care center in Thailand, between January 2016 and September 2019. The study population comprised patients with AF who received intravenous amiodarone therapy. Incidence and associated risk factors for QTc interval prolongation and TdP were assessed using multivariable logistic regression analysis.

Results: A total of 2,944 patients were included in the analysis. Among these, 49 cases of intravenous amiodarone-associated QTc interval prolongation or TdP were identified (33 (1.12%) and 16 (0.54%) cases, respectively), corresponding to an overall incidence of 1.66% (95% confidence interval (CI): 1.23 - 2.19). Multivariable analysis revealed that diabetes mellitus (adjusted odds ratio (aOR): 1.85; 95% CI: 1.02 - 3.38; p-value = 0.045), history of stroke (aOR: 3.09; 95% CI: 1.26 - 7.57; p-value = 0.014), use of antipsychotic medications (aOR: 3.07; 95% CI: 1.64 - 5.74; p-value < 0.001), and use of anticholinergic medications (aOR: 3.89; 95% CI: 1.54 - 9.85; p-value = 0.004) were significantly associated with an increased risk of QTc interval prolongation and TdP following amiodarone therapy for AF patients.

Conclusion: Although the incidence of QTc interval prolongation and TdP related to intravenous amiodarone therapy in patients with AF was relatively low, the risk was significantly elevated in individuals with diabetes mellitus, a history of stroke, or concurrent use of antipsychotic or anticholinergic agents. These findings underscore the importance of vigilant risk assessment and monitoring in clinical practice to mitigate the potential for intravenous amiodarone-induced arrhythmic complications.

This letter suggests that relabelling often reflects documentation differences rather than diagnostic error. It highlights higher mismatch rates in paediatric and neurological patients and considers whether improved coding systems or earlier access to investigations could reduce these gaps. Future studies assessing diagnostic confidence at admission and stronger collaboration between emergency and inpatient teams may also help improve concordance.

Cardiac arrest is a life-threatening condition with a high mortality rate, necessitating prompt recognition and treatment of reversible causes to enhance patient survival. Point-of-care ultrasound (POCUS) has emerged as a useful tool that contributes to optimizing resuscitative efforts. This imaging modality offers real-time visualization that assists in detecting reversible causes such as cardiac tamponade, pulmonary embolism, tension pneumothorax and hypovolemia. This review aims to explore the expanding role of ultrasound in the assessment and management of cardiac arrest, emphasizing its utility in identifying cardiac arrest, differentiating between true pulseless electrical activity (PEA) and pseudo-PEA, detecting the reversible causes, guiding clinical decision-making, and potentially predicting outcomes. A comprehensive literature search was performed using the PubMed database from inception to April 2025. Articles were selected based on their relevance to the role and applications of POCUS in cardiac arrest.