BMC Emergency Medicine最新文献

Background: Sepsis is a critical emergency condition characterized by life-threatening organ dysfunction due to a dysregulated response to infection. In the fast-paced emergency department (ED) setting, rapid identification and prompt initiation of treatment within the initial hours following sepsis onset are critical for reducing mortality and improving patient outcomes. However, a timely and accurate diagnosis remains a significant challenge in emergency medicine. Biomarkers such as procalcitonin (PCT) and presepsin (P-SEP) have been proposed as tools to distinguish sepsis from other non-infectious inflammatory conditions frequently encountered in the ED, though their diagnostic effectiveness remains controversial. This study aimed to evaluate the diagnostic performance of PCT and P-SEP for diagnosis patients with sepsis.

Methods: A comprehensive systematic search was conducted across the Cochrane Central Register of Controlled Trials, PubMed, and Scopus databases up to April 1st, 2024 and updated on June 30th, 2025. Studies reporting sensitivity and specificity of PCT and P-SEP for sepsis detection among patients in acute and emergency settings were included. Hierarchical modeling techniques were utilized to pool data for sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) along with their 95% confidence intervals (CIs).

Results: Thirty-eight observational studies met inclusion criteria. The pooled sensitivities and specificities for detecting sepsis using PCT were 0.78 (95% CI: 0.74-0.81) and 0.77 (95% CI: 0.71-0.82), respectively. Similarly, for P-SEP, pooled sensitivity and specificity were 0.82 (95% CI: 0.77-0.86) and 0.78 (95% CI: 0.73-0.83), respectively. No statistically significant differences were identified between PCT and P-SEP regarding sensitivity (p = 0.169) or specificity (p = 0.792). The summary receiver operating characteristic analysis yielded an AUROC of 0.84 (95% CI: 0.81-0.87) for PCT and 0.87 (95% CI: 0.84-0.90) for P-SEP.

Conclusions: Both PCT and P-SEP represent reliable biomarkers for early and accurate sepsis detection in acute and ED settings, demonstrating comparable diagnostic performance. Their integration into routine ED assessment protocols may support timely clinical decision-making and prompt initiation of appropriate treatment strategies.

Background: Carbon monoxide (CO) poisoning is a leading cause of toxicological emergencies worldwide. Although carboxyhemoglobin (COHb) levels are traditionally used to confirm exposure, they often fail to reflect clinical severity. This study aimed to evaluate the diagnostic performance of serum lactate and hematological parameters in predicting the need for hyperbaric oxygen therapy (HBOT) among patients with acute CO poisoning.

Materials and methods: This retrospective cross-sectional study included 292 adult patients with confirmed CO poisoning admitted to a tertiary emergency department between 2020 and 2024. Patients were categorized according to HBOT administration. Laboratory values including lactate, COHb, and hematologic parameters and indices were analyzed. Multivariable logistic regression and ROC curve analysis were used to assess predictive performance.

Results: Of the 292 patients, 94 (32.2%) received HBOT. Serum lactate and COHb were significantly higher in the HBOT group (p < 0.001 for both) and were identified as independent predictors of HBOT requirement (lactate OR = 2.04; COHb OR = 1.32). AUC for lactate alone was 0.754; combining lactate with hematologic markers modestly improved AUC to 0.769. The most robust model, incorporating lactate and COHb, achieved an AUC of 0.936. Hematologic markers alone showed limited predictive value.

Conclusion: Serum lactate, particularly when combined with COHb, provides strong diagnostic value in predicting HBOT need in CO poisoning. The integration of these readily available biomarkers may improve triage decisions in emergency care.

Introduction: Determining the triage level of patients upon their arrival at the hospital emergency department is highly important for identifying high-risk patients and allocating resources to them. This issue can be of even greater importance in patients presenting with cardiac-related symptoms. This study was conducted to predict the triage level of patients presenting with cardiac-related symptoms using machine learning methods and to compare the performance of different approaches.

Methods: This prospective study was conducted in 2024 in three main steps. In the first step, a literature review was performed, and the factors influencing patient triage levels were extracted from previous studies. Then the identified factors from the literature review were presented to experts for their opinion, and the final influential factors were determined based on their feedback. In the second step, patient data were collected from the triage unit of a specialized cardiac hospital. In the third and final step, the collected data were preprocessed and then analyzed using Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB) machine learning methods.

Results: After reviewing the literature and surveying experts, the confirmed factors were finally identified, and data related to 1862 patients were collected. 52% of the participants in this study were male. RF achieved the highest performance with a test accuracy of 93.57%, Cohen's Kappa of 0.82, and a weighted F1-score of 0.93, followed by GB (accuracy = 90.08%, Kappa = 0.73) and SVM (accuracy = 86.6%, Kappa = 0.66). The most influential factors on patients' triage levels included: the type of high-risk condition that elevates a patient to Level 2, need for life-saving intervention, having high-risk conditions (what condition), chief complaint, level of consciousness, and diseases.

Conclusion: In this study, five machine learning models were utilized for the triage of patients presenting with cardiac-related symptoms. The results of the study indicated that these algorithms had a good ability to discriminate between patients with different triage levels. The Random Forest method performed slightly better than the other techniques. These techniques can be used to differentiate between low-risk and high-risk patients and to allocate resources to high-risk patients.

Background: Injuries within the emergency services populations are unfortunately common. Effective injury reduction programs need to be designed based on the profile of common injuries. Therefore, this study aimed to profile the injuries suffered within a state Emergency Services Agency which comprised Ambulance, Fire and Rescue, Rural Fire, volunteer emergency State Emergency Services (SES), and the Communication Centre (CC).

Methods: A retrospective cohort analysis was conducted on the entirety of an Australian State Emergency Service Agency injury database over a ten-year period (2012-2022). Records were extracted with details including (a) the total number; (b) the bodily site; (c) the nature; (d) and mechanism of injury. Total injuries were converted into injuries per 1000 full-time equivalent (FTE) years of service and incidence rate (IR) and ratios (IRR) were calculated per service.

Results: In total, there were 2,703 physical injuries reported by the agency, with Ambulance Services sustaining over half the injuries (57.5%). The most common body site, nature of injury, and mechanism was the lower back (27.9%), soft tissue (59.7%), and body stressing (45.5%), respectively. Based on 1000 FTE years, SES had the highest IR of 2054.2 followed by Rural Fire (IR = 1295.1), Communications (IR = 753.7), Ambulance Services (IR = 566.3), and Fire and Rescue (IR = 183.7). State Emergency Services sustained the highest IRR of 3.64 [3.13-4.22] when compared to Ambulance Services. The age group most likely to be injured were 45-49 years of age (17%), with males suffering the majority (65%) of injuries.

Conclusion: State Emergency Services present with injury rates above those of other emergency services personnel. These findings lay the groundwork for customised injury prevention strategies to promote better occupational safety across emergency service populations. Tailored injury prevention strategies may decrease subsequent time off due to injury.