World journal of emergency medicine最新文献

Background: In recent years, with the ban on paraquat, the use of diquat (DQ) as a substitute has significantly increased, leading to a corresponding increase in DQ poisoning cases. This study aimed to identify relevant risk factors affecting patient prognosis and provide a basis for the assessment of patient prognosis.

Methods: Patients with DQ poisoning were included from September 2020 to December 2023, and data were extracted from their electronic medical records on the first day of hospitalization. Least Absolute Shrinkage and Selection Operator (LASSO) regression and binary multivariate logistic regression analyses were performed on the collected clinical data to identify risk factors.

Results: A total of 117 patients with acute DQ poisoning were included, and were categorized into two groups based on their 28-day outcomes: survival group (n=67) and non-survival group (n=50). There were no statistically significant differences between the two groups in terms of sex, lymphocyte count, platelet-to-lymphocyte ratio, or blood purification rate (P>0.05). The analysis revealed that age (odds ratio [OR] 1.094, 95% confidence interval [95% CI] 1.022-1.171), blood drug concentration (OR 3.659, 95% CI 1.846-7.252), lactate (OR 1.686, 95% CI 1.062-2.678), neutrophil-to-lymphocyte ratio (NLR) (OR 1.101, 95% CI 1.017-1.192), albumin (OR 1.275, 95% CI 1.107-1.468), and aspartate aminotransferase (AST) (OR 1.027, 95% CI 1.005-1.051) were the risk factors for mortality.

Conclusion: This study identified key risk factors for 28-day mortality in patients with acute DQ poisoning, which may provide valuable guidance for clinical treatment, particularly for emergency physicians.

Background: The problem of prolonged emergency department length of stay (EDLOS) is becoming increasingly crucial. This study aims to develop a machine learning (ML) model to predict EDLOS, with EDLOS as the outcome variable and demographic characteristics, triage level, and medical resource utilization as predictive factors.

Methods: A retrospective analysis was performed on the patients who visited the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to September 2021, and a total of 321,012 cases were identified. According to the inclusion and exclusion criteria, 187,028 cases were finally included in the analysis. ML analysis was performed using R-squared (R²), and the predictive factors and the EDLOS were used as independent variables and dependent variables, respectively, to establish models. The performance evaluation of the ML models was conducted through the utilization of the mean absolute error (MAE), root mean square error (RMSE), and R², enabling an objective comparative analysis.

Results: In the comparative analysis of the six ML models, light gradient boosting machine (LightGBM) model demonstrated the lowest MAE (443.519) and RMSE (826.783), and the highest R² value (0.48), indicating better model fit and predictive performance. Among the top 10 predictive factors associated with EDLOS according to the LightGBM model, the emergency waiting time, age, and emergency arrival time had the most significant impact on the EDLOS.

Conclusion: The LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.

Background: Community-acquired pneumonia (CAP) represents a significant public health concern due to its widespread prevalence and substantial healthcare costs. This study was to utilize an integrated proteomic and metabolomic approach to explore the mechanisms involved in severe CAP.

Methods: We integrated proteomics and metabolomics data to identify potential biomarkers for early diagnosis of severe CAP. Plasma samples were collected from 46 CAP patients (including 27 with severe CAP and 19 with non-severe CAP) and 19 healthy controls upon admission. A comprehensive analysis of the combined proteomics and metabolomics data was then performed to elucidate the key pathological features associated with CAP severity.

Results: The proteomic and metabolic signature was markedly different between CAPs and healthy controls. Pathway analysis of changes revealed complement and coagulation cascades, ribosome, tumor necrosis factor (TNF) signaling pathway and lipid metabolic process as contributors to CAP. Furthermore, alterations in lipid metabolism, including sphingolipids and phosphatidylcholines (PCs), and dysregulation of cadherin binding were observed, potentially contributing to the development of severe CAP. Specifically, within the severe CAP group, sphingosine-1-phosphate (S1P) and apolipoproteins (APOC1 and APOA2) levels were downregulated, while S100P level was significantly upregulated.

Conclusion: The combined proteomic and metabolomic analysis may elucidate the complexity of CAP severity and inform the development of improved diagnostic tools.

Background: Identifying and managing medical emergencies presents challenges in healthcare, where familiarity with established algorithms is essential for high-quality care. This study assessed healthcare professionals' understanding of the latest resuscitation guidelines and explored their views on lifelong training models.

Methods: This cross-sectional study used two multiple-choice questionnaires with 50 questions developed by academic emergency and critical care consultants based on the 2021 Consensus on Science with Treatment Recommendations (CoSTRs) by the International Liaison Committee on Resuscitation (ILCOR). Healthcare staff involved in emergency coverage completed assessments on emergency management, self-evaluated their knowledge, and shared perspectives on continuous workplace education.

Results: Of the 1,427 distributed questionnaires, 1,034 (72.5%) were completed. Knowledge gaps were more pronounced for pediatric algorithms from the European Resuscitation Council (ERC) and American Heart Association (AHA) compared to adult protocols (P<0.001). In multivariate logistic regression, being a physician, holding a Master of Science (MSc) degree, and younger age were independently associated with passing scores ≥70% (all P<0.001). Most participants (97.3%) favored brief, employer-funded teamwork refresher sessions every 4-6 months over the current four-year training model (0.6%) (P<0.001).

Conclusion: This study highlights healthcare life support providers' insufficient expertise in current resuscitation guidelines. The importance of short-format retraining, upskilling, and reskilling programs with post-training assessments is evident, as most respondents expressed a strong learning motivation to participate if employer-funded.