BMC Emergency Medicine最新文献

Introduction: The provision of efficient and integrated emergency services constitutes a vital cornerstone for reducing mortality rates and improving clinical outcomes for patients and casualties within complex healthcare systems. Effective engagement between hospital-based and pre-hospital emergency services is crucial for the delivery of higher quality and more appropriate patient care. This study was conducted with the aim of analyzing the factors affecting organizational engagement between pre-hospital and hospital emergency departments.

Methods: This was a qualitative study employing a conventional content analysis approach, conducted across pre-hospital emergency services and affiliated educational hospitals of Shahid Sadoughi University of Medical Sciences, Iran, in 2025. Participants included 38 experienced stakeholders in the field, encompassing managers, supervisors, physicians, nurses, and emergency medical technicians. These individuals were selected using purposive sampling until data saturation was achieved. Data were collected through semi-structured interviews and subsequently analyzed using MAXQDA software (Version 22). The credibility and trustworthiness of the findings were assessed according to Lincoln and Guba's criteria.

Results: Data analysis revealed that organizational interaction between pre-hospital and hospital emergency services faced significant barriers, primarily attributable to poor managerial coordination, cognitive and professional limitations, and gaps in clinical practice. Specifically, the lack of effective inter-organizational communication, weak leadership in conflict resolution, and insufficient structural and human resource support were identified as key managerial obstacles. In the domain of cognitive and professional limitations, negative attitudes, knowledge and skills gaps, and decision-making biases-often arising under conditions of high workload or limited information-were found to reduce mutual understanding and collaboration between personnel across the two sectors. Regarding clinical practice gaps, the absence of standardized patient handover protocols, inconsistencies in the implementation of care processes, and deficiencies in initial clinical assessments disrupted care continuity and diminished overall service quality.

Conclusion: The findings of this study provide scientific guidance for decision-makers seeking to establish a more integrated, efficient, and safer emergency system. Enhancing organizational engagement within the emergency system necessitates a multifaceted and policy-driven approach, wherein policymakers play a pivotal role in reforming organizational structures, empowering human resources, and standardizing operational processes.

Introduction: Out-of-hospital cardiac arrest (OHCA) is a critical medical emergency where rapid access to automated external defibrillators (AED) can significantly improve survival rates. However, there is currently a lack of well-established frameworks and guidelines concerning the optimal placement of AED. Additionally, historical data on the locations of OHCA incidents is often unavailable or incomplete. This study seeks to address these gaps by analyzing the most effective AED placement strategies and evaluating the impact of additional AED locations on suspected OHCA cases. To achieve this, a machine learning (ML) model is developed that relies exclusively on demographic and infrastructural factors, without the need for historical OHCA location data.

Methods: In this data-driven predictive modelling study, 5,076 alerts of suspected OHCA and 95 AED locations in Freiburg were analysed (October 7, 2018, to May 28, 2024). Demographic and infrastructural data were integrated into a three-step approach to identify and prioritize optimal AED placements. A Decision Tree was trained to predict OHCA risk at possible locations, followed by the application of a greedy algorithm to determine AED locations. The models were validated using several performance metrics and historical OHCA data to ensure accuracy. Additionally, different scenarios were evaluated to maximize AED coverage of OHCA incidents.

Results: Optimizing AED placement using predicted data increased coverage from 21.6% to 42.4%, without adding more devices. The ML model's coverage was only 6.7% lower than that achieved using historical alert data. Adding 19 AEDs (a 20% increase) to the existing network raised coverage to 30.5%.

Conclusion: The findings demonstrate the feasibility of using ML models for AED placement in regions lacking comprehensive historical data. Integrating advanced ML techniques can further refine strategies for AED deployment in urban areas, ultimately improving emergency response effectiveness.

Trial registration: The trial is registered with the German Clinical Trials Register (DRKS, ID: DRKS00016625 15/04/2019 and DRKS00032957 30/10/2023), which is a WHO primary register.

Background: Most early warning scores were derived in heterogeneous acutely ill populations and are not specifically tailored to patients with suspected sepsis in the emergency department (ED). In sepsis, respiratory rate (RR) is frequently elevated as part of the pathophysiologic response, suggesting that sepsis-focused tools may require recalibrated RR thresholds. We therefore aimed to develop and evaluate the Respiratory National Early Warning Score (R-NEWS), a sepsis-oriented modification emphasizing respiratory parameters, to improve prediction of 28- and 7-day mortality among ED patients with suspected sepsis.

Methods: We conducted a prospective cohort study in the ED of Thammasat University Hospital, enrolling adults with suspected sepsis and excluding those with cardiac arrest or do-not-resuscitate orders. Multivariable backward stepwise logistic regression identified significant predictors of 28-day mortality, which were then used to construct the R-NEWS scoring system. Predictive performance was evaluated and compared with NEWS, qSOFA, and SIRS using the area under the receiver operating characteristic curve (AuROC).

Results: A total of 1,348 patients were analyzed; 28-day mortality was 13.4%. Five independent predictors were identified: respiratory rate, need for supplemental oxygen, systolic blood pressure, heart rate, and Glasgow Coma Scale. R-NEWS demonstrated good calibration (Hosmer-Lemeshow p = 0.474) and higher discrimination than NEWS, qSOFA, and SIRS. The AuROC of R-NEWS was 0.72 (95% CI, 0.68, 0.76) for 28-day mortality and 0.76 (95% CI, 0.71, 0.81) for 7-day mortality. Internal validation using bootstrapping yielded consistent results. High-risk R-NEWS (≥ 7) was associated with a 28-day mortality of 28.9%, compared with 3.9% in the low-risk group (≤ 3).

Conclusion: R-NEWS, a simplified five-parameter score emphasizing respiratory function, demonstrated favorable performance for predicting short- and intermediate-term mortality in ED patients with suspected sepsis. Used alongside clinical judgment, R-NEWS may aid early risk stratification and sepsis management. External, multicenter validation and prospective evaluation are warranted before widespread implementation.

Background: Emergency department (ED) overcrowding has become a global public health concern, underscoring the importance of rapid and reliable risk stratification tools. Early warning scores are widely used to identify patients at risk of deterioration and mortality. The recently developed International Early Warning Score (IEWS), which incorporates age and sex adjustments into the National Early Warning Score (NEWS) model, has shown promising results and has undergone initial external validation in a Danish cohort; however, no prospective external validation has yet been conducted, and broader international validation remains limited. This study aimed to evaluate the performance of IEWS compared with NEWS in predicting in-hospital mortality, 30-day mortality, and ICU admission among adult ED patients.

Methods: This prospective observational cohort study was conducted between July and August 2024 in a tertiary university hospital ED with an annual census of ~ 70,000 visits. Adult patients presenting to the ED were included, while trauma cases, patients without vital signs on arrival, interhospital transfers, and cases with incomplete data were excluded. IEWS and NEWS were calculated at presentation. The primary outcome was all-cause in-hospital mortality; secondary outcomes included 30-day mortality and ICU admission.

Results: A total of 8,666 patients were analyzed. The median age was 40 years (IQR: 26-58), and 51.5% were female. In-hospital mortality was 1.5% (n = 134), and 30-day mortality was 1.9% (n = 163). IEWS demonstrated excellent discriminative ability for in-hospital and 30-day mortality (AUC: 0.944 and 0.930, respectively), and good performance for ICU admission (AUC: 0.876). In contrast, NEWS showed good performance for in-hospital and 30-day mortality (AUC: 0.884 and 0.848, respectively) and moderate performance for ICU admission (AUC: 0.781). IEWS consistently outperformed NEWS across all outcomes (p < 0.05, DeLong's test).

Conclusion: IEWS outperformed NEWS in predicting in-hospital mortality, 30-day mortality, and ICU admission among non-traumatic ED patients. Given its high sensitivity, specificity, and overall discriminative performance, IEWS may serve as a reliable bedside tool for patient risk stratification in the ED. Large-scale multicenter studies are needed to confirm its generalizability across diverse populations.

Clinical trial number: Not applicable.