International Journal of Emergency Medicine最新文献

Background: Penetrating thoracic and abdominal trauma presents a diagnostic and therapeutic challenge in the emergency department (ED). Traditionally managed with open laparotomy or thoracotomy, advances in minimally invasive surgery (MIS) have enabled safe, effective, and rapid diagnosis and intervention. We present a case series of four patients with penetrating gastric, hepatic, and pulmonary injuries successfully managed using MIS, highlighting its role in emergency care.

Case presentations: Four hemodynamically stable patients presented to the ED with penetrating abdominal or thoracic trauma: (1) Stab wound to epigastrium: Diagnostic laparoscopy revealed a 1 cm anterior gastric body perforation, repaired laparoscopically. The patient recovered uneventfully and remained asymptomatic at 3-year follow-up. (2) Gunshot to upper abdomen: Laparoscopic wedge resection of a gastric cardia perforation was performed. Recovery was uneventful, with 1-year follow-up showing no complications. (3) Gunshot to right upper abdomen with hepatic injury: Laparoscopic hemostasis and bullet extraction under fluoroscopy were successfully performed. Four-year follow-up was uneventful. (4) Gunshot to left upper chest: Video-assisted thoracoscopic left upper lobe segmentectomy and bullet extraction were carried out. Five-year follow-up revealed preserved pulmonary function and no complications.

Discussion: Early identification and triage in the ED, combined with MIS, enabled rapid diagnosis and definitive treatment while minimizing morbidity. Laparoscopy and thoracoscopy provide excellent visualization, reduce unnecessary open procedures, and shorten hospital stays.

Conclusion: MIS is a safe, effective, and feasible approach for selected hemodynamically stable penetrating thoracic and abdominal injuries in the ED. Careful patient selection and surgical expertise are essential for optimizing outcomes.

Background and objectives: Prehospital emergency dispatch centers play a pivotal role in disaster response, yet their preparedness is often inadequate, particularly in low-resource settings such as Iran. This study aimed to explore the factors influencing the disaster preparedness of these centers through a qualitative approach.

Methods: A conventional content analysis was conducted between October 2024 and May 2025. Twenty experts and managers from Emergency Medical Services (EMS) and dispatch centers were selected using purposive and snowball sampling. Data were gathered through semi-structured interviews and analyzed inductively using MAXQDA-2020. Trustworthiness was ensured following Guba and Lincoln's criteria.

Results: In this study, thirteen main categories were extracted from the data. The analysis revealed that disaster preparedness in emergency dispatch centers is influenced by a complex interaction of human, organizational, and environmental factors. Among the most influential dimensions were planning and training, communication and coordination systems, and the psychological and operational readiness of personnel. Collectively, these components determine the centers' ability to maintain functionality and ensure an effective response under disaster conditions.

Conclusion: Strengthening the preparedness of emergency dispatch centers requires a systems-based approach that integrates comprehensive planning, continuous training, resilient communication infrastructure, and mental health support for telecommunicators. However, as a qualitative study, the findings are context-specific and may not be generalizable to all settings. Nevertheless, they provide an in-depth understanding of the multidimensional factors influencing preparedness and response, offering valuable insights for policymakers, health system managers, and model developers in disaster management.

Background: Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) are life-threatening acute complications of diabetes. Up to one-third of patients present with overlapping features of both syndromes, complicating diagnosis and management. This overlap is associated with higher mortality than isolated DKA or HHS.

Objective: This narrative review synthesizes current evidence on the epidemiology, pathophysiology, and management of DKA-HHS overlap in both adult and pediatric populations with either type 1 or type 2 diabetes, highlighting the distinct challenges in recognition, treatment, and outcomes across these groups.

Findings: While DKA is defined by ketosis and metabolic acidosis, and HHS by profound hyperglycemia and hyperosmolality, overlap presentations combine both abnormalities. These patients face increased risks of cerebral edema, thromboembolism, and acute kidney injury. Standardized management protocols remain lacking, and treatment must balance fluid resuscitation, insulin therapy, and electrolyte correction, with distinct approaches for adults and children. Emerging issues such as SGLT2 inhibitor-induced euglycemic DKA and disparities in outcomes between high- and low-resource settings further complicate care.

Conclusion: DKA-HHS overlap represents a high-risk clinical phenotype requiring early recognition and individualized therapy. Consensus guidelines, risk stratification tools, and studies of fluid and insulin strategies are urgently needed to improve outcomes in this vulnerable population.

Background: Artificial Intelligence (AI) has been increasingly explored in healthcare, particularly in emergency department (ED) triage. This study aimed to evaluate the effectiveness of the AI chatbot ChatGPT in triaging patients, focusing on its accuracy, safety, efficiency, and impact on patient care.

Methods: A prospective observational study was conducted at the ED of King Saud Medical City (KSMC) in Riyadh, Saudi Arabia, with a sample size of 138 patients. Patients requiring immediate resuscitation were excluded. ED physicians assigned triage scores using the Canadian Triage and Acuity Scale (CTAS), followed by AI-generated scores for the same patients. In cases of discrepancy, the final decision by the senior ED consultant was considered the gold standard. The study assessed inter-rater reliability between AI and human raters and evaluated the accuracy of each compared to the consultant's assessment.

Results: The results indicated a high agreement rate (85.61%) between ChatGPT and ED physicians, with substantial inter-rater reliability (κ = 0.780, 95% Confidence Interval [CI] 0.676-0.884, p < 0.001). Agreement between ED physicians and consultants was at 63.9%, with moderate reliability (κ = 0.406, 95% CI 0.006-0.806, p = 0.018). Consultants assigned lower acuity levels than physicians in most cases. ChatGPT's accuracy compared to the consultant was 42.86%, with slight reliability, showing a tendency to overestimate acuity, particularly in critical cases. However, it performed better in mid-range acuity levels.

Conclusion: The findings suggested that AI could support ED triage by aligning closely with human decision-making. However, its overestimation of severity could lead to over-triaging and increased resource use. Limitations included a small sample size and the use of a general AI model not specifically trained for medical triage. Future research should focus on AI models tailored for ED triage to improve reliability and clinical applicability.

Background: Timely access to a neurologist is essential for optimal management of acute stroke. To address disparities in neurological care within the SEHA Healthcare Network, the LEO360® AI-assisted tele-stroke robot was implemented at Sheikh Tahnoon bin Mohammed Medical City (STMC) and Al Tawam Hospital.

Methods: This case series presents six patients evaluated remotely via the LEO360® platform. Key metrics included consultation time, transfer rates, and system performance. Data were contextualized using pre-implementation benchmarks, and both AI functionalities and telepresence capabilities were analyzed. Challenges, ethical considerations, and system limitations were also examined.

Results: The average neurologist consultation time was 10.7 minutes. In 80% of cases, unnecessary interfacility transfers were avoided. The integration of AI-assisted decision support enhanced assessment efficiency and diagnostic confidence.

Conclusion: The LEO360® tele-stroke system demonstrates strong potential to improve access, efficiency, and accuracy in acute stroke management. Its successful implementation underscores the scalability of AI-assisted telemedicine in regions facing neurology workforce shortages, offering a sustainable model for acute neurological care.