BMC Emergency Medicine最新文献

Background: Long-COVID research to date focuses on outcomes in non-hospitalised vs. hospitalised survivors. However Emergency Department attendees (post-ED) presenting with acute COVID-19 may experience less supported recovery compared to people admitted and discharged from hospital (post-hospitalised group, PH).

Objective: We evaluated outcomes and predictors of specialty care referrals (SCR) in those with ongoing symptomatic Long-COVID, comparing post-ED and PH adults.

Methods: This prospective observational cohort study evaluates 800 PH and 484 post-ED adults from a single hospital in London, United Kingdom. Participants had either confirmed laboratory-positive SARS-CoV-2 infection or clinically suspected acute COVID-19 and were offered post-COVID clinical follow-up at approximately six weeks after their ED attendance or inpatient discharge, to assess ongoing symptoms and support recovery. Multiple logistic regression determined associations with specialist care referrals (SCR) to respiratory, cardiology, physiotherapy (including chest physiotherapy), and mental health services.

Results: Presence of at least one Long-COVID symptom was lower in adults attending ED services with acute COVID-19 compared to those hospitalised (70.1% post-ED vs. 79.5% PH adults, p < 0.001). Total number of Long-COVID symptoms was associated with increased SCR in all patients (adjusted odds ratio (aOR) = 1.26, 95%CI:1.16, 1.36, p < 0.001), with post-ED adults more likely to need a SCR overall (aOR = 1.82, 95%CI:1.19, 2.79, p = 0.006). Post-ED adults had higher SCR to both physiotherapy (aOR = 2.59, 95%CI:1.35, 4.96, p = 0.004) and mental health services (aOR = 3.84, 95%CI:2.00, 7.37, p < 0.001), with pre-existing mental illness linked to the latter (aOR = 4.08, 95%CI:1.07, 15.6, p = 0.04).

Conclusions: We demonstrate greater specialist care referrals to mental health and physiotherapy services in patients attending the ED and discharged with acute COVID-19, compared to those admitted, despite lower ongoing COVID-19 symptom burden. Total number of symptoms, pre-existing co-morbidity such as smoking status, cardiac co-morbidities, and mental health illnesses may predict those requiring healthcare input. This information may enable better post-COVID support for ED attendees, a distinct group who should not be neglected when preparing for future pandemics.

Trial registration: This study had HRA approval (20/HRA/4928).

Objectives: Dysnatremia is a critical electrolyte disturbance that can significantly impact the prognosis of trauma patients by influencing fluid balance, neurological function, and hemodynamics. Although sodium disorder is common in hospitalized patients, few studies have specifically examined the incidence of dysnatremia in patients presenting to the emergency department for post-earthquake trauma. The aim of this study is to evaluate the incidence of dysnatremia and the prognosis of patients with dysnatremia in trauma patients admitted to our center after the Kahramanmaraş earthquake.

Materials and methods: Between February 6th, 2023 and February 20th, 2023, a total of 422 patients with earthquake-related crush injuries after the Kahramanmaras earthquake were retrospectively analyzed. Patients were divided into two groups: the survivor group and the exitus group. Then, patients with dysnatremia were evaluated. Age, gender, the city where patients came from, type of presentation, injured organ systems and extremities, laboratory findings, ward admission, intensive care admission, GCS, MESS, ISS, RTS, TRISS, and hemodialysis sessions were analyzed.

Results: A total of 422 earthquake victims with crush injuries were included in the study. Dysnatremia was present in 28% of the patients, and these patients had a longer hospital stay. GCS was lower, MESS and ISS values were higher, and survival rates according to TRISS were lower. Multiple extremity trauma, crush syndrome, and dialysis rates were more common in the dysnatremia group. Additionally, fasciotomy, amputation and mortality rates were higher in the dysnatremia group.

Conclusion: Dysnatremia is a common finding in patients with crush injuries and is associated with more severe trauma and poorer clinical outcomes. This study highlights the need for close monitoring and management of sodium disturbances in trauma patients, not only in the context of earthquakes but across various disaster scenarios. Recognizing and addressing dysnatremia can contribute to improved patient outcomes in disaster and emergency settings.

Background: Acute non-traumatic chest pain is one of the common complaints in the emergency department and is closely associated with fatal disease. Triage assessment urgently requires the use of simple, rapid tools to screen patients with chest pain for high-risk condition to improve patient outcomes.

Methods: After data preprocessing and feature selection, univariate and multiple logistic regression analyses were performed to identify potential predictors associated with acute non-traumatic chest pain. A nomogram was built based on the predictors, and an internal evaluation was performed using bootstrap resampling methods. The model was also externally validated in this center. Furthermore, the model results were risk-stratified using the decision tree analysis to explore the corresponding triage level. Subsequently, we developed an online visualization tool based on the model to assess the risk of high risk in patients with chest pain.

Results: Multiple logistic regression analysis showed that age, smoking, coronary heart disease, hypertension, diabetes, hyperlipidemia, pain site, concomitant symptoms, and electrocardiograph, all of which are independent predictors of high-risk chest pain patients. The AUC of our model in the development and validation groups was 0.919 (95%CI: 0.891 ~ 0.974) and 0.904 (95%CI: 0.855 ~ 0.952). Moreover, our model demonstrated better outcomes in terms of accuracy/sensitivity in both cohorts (81.9%/85.2% and 94.8%/78.5%). The calibration curve shows a high degree of agreement between the predicted and actual probabilities. Decision curve analysis clarified that our model had higher net gains across the entire range of clinical thresholds. Afterward, we developed an online tool, which is used in the triage link to facilitate nurses to screen people with high-risk chest pain.

Conclusion: We proposed an accurate model to predict the high-risk populations with chest pain, based on which a simple and rapid online tool was developed and provided substantial support for its application as a decision-making tool for the emergency department.

Registration: The study protocol was approved by the Ethics Committee Board of Fujian Provincial Hospital.

Clinical trial registration number: ChiCTR2200061918.

Background: Volunteers providing nursing services are among the first individuals to arrive at the scene after an incident; therefore, they must use their skills and capabilities to provide necessary care for the injured to prevent problems from worsening and complications from arising. Consequently, having structured empowerment courses for volunteers before disasters seems essential. This research aimed to determine the dimensions and components of empowering volunteer nursing service providers in disasters.

Methods: This qualitative study was conducted using a conventional qualitative content analysis approach. Purposeful sampling with maximum variation continued until data saturation was achieved. The participants in this study were 25 individuals. Data collection was performed using semi-structured interviews. The conventional content analysis approach by Graneheim and Lundman was utilized for data analysis. After each interview, the text was entered into MAXQDA software version 2020, and semantic units were identified and coded, with codes categorized based on their similarities. To ensure the data's trustworthiness, we applied key criteria including credibility, dependability, confirmability, transferability, and Authenticity throughout the research process.

Results: Among the 25 participants, 16 were male, and nine were female. The Mean age was 45.2 ± 5.6 years, and the Mean work experience was 19.08 ± 4.5 years. After coding the interviews, 253 initial codes were extracted. By merging these codes, 43 subcategories, 15 categories, and six themes were identified. Extracted themes include Infrastructure Initiatives, Effective management, Research development and knowledge management, Cultural Action Measures, Policymaking and legal affairs, Individual Competency Areas.

Conclusions: This study highlights the various dimensions and components that empower volunteers providing nursing services during disasters. Training and coordination between nursing managers and other leaders in health sectors are needed to ensure that these volunteers are equipped to provide frontline care in emergencies. Healthcare managers and policymakers can use this study's results to create supportive legislation and training programs to enhance the capabilities of volunteer nursing providers in disasters.

Background: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.

Methods: Data from 4,109 trauma patients at Soroti Regional Referral Hospital, a rural hospital in Uganda, were used to train and evaluate four ML models: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The models were assessed in regard to accuracy, precision, recall, F1-score, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic curve). Additionally, a multinomial logistic regression model using the KTS was developed as a benchmark for the ML models.

Results: All four ML models outperformed the KTS model, with the RF and GB both achieving AUC-ROC values of 0.91, compared to 0.62 (95% CI: 0.61-0.63) for the KTS (p < 0.01). The RF model demonstrated the highest accuracy at 0.69 (95% CI: 0.68-0.70), while the KTS-based model showed an accuracy of 0.54 (95% CI: 0.52-0.55). Sex, hours to hospital, and age were identified as the most significant predictors in both ML models.

Conclusion: ML models demonstrated superior predictive capabilities over the KTS in predicting triage decisions, even when utilising a limited set of injury information about the patients. These findings suggest a promising opportunity to advance trauma care in LMICs by integrating ML into triage decision-making. By leveraging basic demographic and clinical data, these models could provide a foundation for improved resource allocation and patient outcomes, addressing the unique challenges of resource-limited settings. However, further validation is essential to ensure their reliability and integration into clinical practice.

Background: Emergency departments (EDs) are high pressure work environments with several psychosocial job demands, e.g., violence, and job resources, e.g., colleague support. So far, the perceptions of working conditions have been compared between doctors and nurses, but there is limited knowledge regarding their respective supervisors. In addition, the violence prevention climate has not been assessed in German EDs before. Thus, the current study focuses on differences in the perceptions of working conditions and the violence prevention climate between the groups of doctor-supervisors, doctor-employees, nurse-supervisors, and nurse-employees within the ED. Further analyses regarding the association between social relations and pressure for unsafe practices are performed, including the moderating role of belonging to one of the aforementioned groups.

Methods: A cross-sectional online survey was carried out among N = 370 participants, who were doctors or nurses from German EDs. The Questionnaire for Psychosocial Risk Assessment (QPRA) and the Violence Prevention Climate Scale (VPCS) were applied. Kruskal-Wallis tests were performed for group comparisons, followed by a hierarchical multiple linear regression model and moderation analyses.

Results: Statistically significant differences between the groups were found for eight out of 13 variables. The highest number of significant pairwise comparisons was found between the groups of doctor-supervisors and nurse-employees. High job demands regarding work intensity and work interruptions became apparent across all groups. Nurse-employees reported the highest social and emotional demands as well as the highest pressure for unsafe practices regarding violence prevention, significantly differing from the other groups on these variables. The variables of supervisor support and social stressors were found to be significantly predictive of pressure for unsafe practices. Furthermore, there was no moderating effect of belonging to one of the above-mentioned groups in the relationships between variables of social relations and pressure for unsafe practices.

Conclusions: Differences found in the current study can help tailor preventive measures according to the needs of distinct professions and positions in order to improve working conditions and the violence prevention climate in EDs. Furthermore, supervisor support should be strengthened while social stressors should be resolved in order to decrease pressure for unsafe practices regarding violence prevention.

Background: Globally, healthcare institutions have seen a marked rise in workplace violence (WPV), especially since the Covid-19 pandemic began, affecting primarily acute care and emergency departments (EDs). At the University Health Network (UHN) in Toronto, Canada, WPV incidents in EDs jumped 169% from 0.43 to 1.15 events per 1000 visits (p < 0.0001). In response, UHN launched a comprehensive, systems-based quality improvement (QI) project to ameliorate WPV. This study details the development of the project's design and key takeaways, with a focus on presenting trauma-informed strategies for addressing WPV in healthcare through the lens of health systems innovation.

Methods: Our multi-intervention QI initiative was guided by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework. We utilized the SEIPS 101 tools to aid in crafting each QI intervention.

Results: Using the SEIPS 3.0 framework and SEIPS 101 tools, we gained a comprehensive understanding of organizational processes, patient experiences, and the needs of HCPs and patient-facing staff at UHN. This information allowed us to identify areas for improvement and develop a large-scale QI initiative comprising 12 distinct subprojects to address WPV at UHN.

Conclusions: Our QI team successfully developed a comprehensive QI project tailored to our organization's needs. To support healthcare institutions in addressing WPV, we created a 12-step framework designed to assist in developing a systemic QI approach tailored to their unique requirements. This framework offers actionable strategies for addressing WPV in healthcare settings, derived from the successes and challenges encountered during our QI project. By applying a systems-based approach that incorporates trauma-informed strategies and fosters a culture of mutual respect, institutions can develop strategies to minimize WPV and promote a safer work environment for patients, families, staff, and HCPs.