Archives of Academic Emergency Medicine最新文献

Introduction: Lead poisoning can present with a spectrum of symptoms, from fatigue to severe multiorgan complication; and Zeolite is known for its ability to remove heavy metals. This study aimed to assess the impact of zeolite on serum lead levels and blood parameters of patients with mild to moderate lead poisoning.

Methods: This double-blind randomized clinical trial evaluated the effects of zeolite on serum lead levels of patients with mild to moderate lead poisoning, conducted between August 2022 and December 2022. The intervention group received oral zeolite tablets in addition to standard treatment, while the control group received only standard treatment. The impact of zeolite administration on serum lead levels and blood parameters was investigated using an appropriate statistical test.

Results: 80 patients with a mean age of 39.84 ± 11.94 (range: 23-75) years were randomized (78.75% male). The two groups were similar regarding age (p = 0.329), sex (p = 0.785), baseline serum lead levels (p = 0.596), and liver enzymes (p = 0.648).The Zeolit group had lower serum lead levels (25.22 ± 13.26 vs. 37.68 ± 15.34; p < 0.001, ES: 0.869) and hematocrit (36.21 ± 7.83 vs. 39.53 ± 5.60; ES: 0.488; p = 0.032) after 2 weeks of treatment.

Conclusion: Zeolite tablets show considerable promise as an adjunct therapy for mild to moderate lead poisoning. They effectively lower serum lead levels without adverse effects. This intervention could reduce the metal burden in the bloodstream and mitigate lead-induced multiorgan damage, offering a more effective and less invasive treatment option.

Introduction: A growing area is the use of ChatGPT in simulation-based learning, a widely recognized methodology in medical education. This study aimed to evaluate ChatGPT's ability to generate realistic simulation scenarios to assist faculty as a significant challenge in medical education.

Method: This study employs a qualitative research design and thematic analysis to interpret expert opinions. The study was conducted in two phases. Scenario generation via ChatGPT and expert review for validation. We used ChatGPT (GPT-4) to create clinical scenarios on cardiovascular topics, including cardiogenic shock, postoperative cardiac tamponade after heart surgery, and heart failure. A panel of five experts, four nurses with expertise in emergency medicine and critical care and an anesthesia specialist, evaluated the scenarios. The experts' feedback, strengths and weaknesses, and proposed revisions from the expert discussions were analyzed via thematic analysis. Key themes and proposed revisions were identified, recorded, and compiled by the research team.

Results: The clinical scenarios were produced by ChatGPT in less than 5 seconds per case. The thematic analysis identified six recurring themes in the experts' discussions: clinical accuracy, the clarity of learning objectives, the logical flow of patient cases, realism and feasibility, alignment with nursing competencies, and level of difficulty. All the experts agreed that the scenarios were realistic and followed clinical guidelines. However, they also identified several errors and areas that needed improvement. The experts identified and documented specific errors, incorrect recommendations, missing information, and inconsistencies with standard nursing practices.

Conclusion: It seems that, ChatGPT can be a valuable tool for developing clinical scenarios, but expert review and refinement are necessary to ensure the accuracy and alignment of the generated scenarios with clinical and educational standards.

Introduction: The diagnostic accuracies of different electrocardiography (ECG) interpretation methods remain unclear. Therefore, this study aimed to systematically evaluate and compare the diagnostic accuracy of prehospital 12-lead ECG interpretation methods for identifying ST-elevation myocardial infarction (STEMI) and activating cardiac catheterization laboratories (CCLs).

Methods: A comprehensive search was conducted in Medline, Scopus, and CINAHL databases up to August 2024. Two reviewers independently selected studies that assessed the diagnostic accuracy of prehospital 12-lead ECG in real-time STEMI identification and CCL activation. Pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated using bivariate generalized mixed-effects regression models or random-effects meta-analysis as appropriate. The quality of the included studies was assessed using the QUADAS-2 tool.

Results: Thirty-six studies involving 67,168 patients were included. Overall, for STEMI identification, the pooled AUC of ECG was 0.96 (95%CI:0.94-0.98), sensitivity was 80% (95% CI, 69-88%), specificity was 97% (95%CI: 94-98%), and DOR was 114 (95%CI: 59-222). Ambulance clinicians achieved the highest DOR (264; 95%CI: 33-2125), followed by transmission method (136; 95%CI, 59-312) and computer-assisted analysis (78; 95%CI: 33-186). Transmission method demonstrated superior specificity (‎0.98; 95%CI: 0.94-0.99‎) and the lowest rates of inappropriate (13.2%; 95% CI: ‎8.6%-19.2%), and false-positive (11.0%; 95%CI: 6.9%-15.0%) CCL activations.

Conclusion: All prehospital ECG interpretation methods yielded acceptable diagnostic accuracy for STEMI identification; however, transmission offered the greatest specificity and fewer unnecessary CCL activations. Adopting transmission-based strategies, where feasible, and enhancing training and decision support for ambulance clinicians may improve prehospital STEMI detection and resource utilization.

Introduction: Ventilation and oxygen delivery during cardiopulmonary resuscitation (CPR) is of paramount importance. This study aimed to compare the effects of manual and mechanical ventilation on respiratory parameters of intubated patients during CPR.

Methods: This randomized controlled clinical trial was conducted in 2024 on 61 intubated patients with neurological disorders admitted to the ICU of educational hospitals. Participants were allocated to either the intervention or the control group using block randomization with a block size of six. The intervention group received mechanical ventilation, while the control group received manual ventilation using bag valve mask (BVM). The effects of manual versus mechanical ventilation during CPR on key physiological and respiratory parameters, including venous blood gases (VBG), end tidal Co2 (ETCO₂), and peripheral oxygen saturation (SpO₂) were compared between groups. Statistical analyses were performed using SPSS version 21.

Results: The study findings indicated no statistically significant differences between the manual and mechanical ventilation groups in terms of venous blood pH levels (P = 0.38), PCO₂ (P = 0.65), and HCO₃ levels (P = 0.47) changes. However, PO₂ (P < 0.001), ETCO₂ (P < 0.05). and SpO₂ (P < 0.001) were more stable and consistently higher in patients receiving mechanical ventilation.

Conclusion: These findings suggest that while pH, PCO₂, and HCO₃ levels did not significantly differ between the two ventilation methods, mechanical ventilation demonstrated superior efficacy in optimizing oxygenation (PO₂ and SpO₂) and regulating ETCO₂ levels.

Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed to summarize AI applications in prehospital emergency care, emergency radiology, triage and patient classification, emergency diagnosis and interventions, pediatric emergency care, trauma care, outcome prediction, as well as the legal and ethical challenges and limitations of AI use in emergency medicine. A comprehensive literature search was conducted in Web of Science, Scopus, and Medline using a wide range of artificial intelligence and machine learning-related keywords combined with terms related to emergency medicine to identify relevant published studies. The findings show that AI-powered tools can assist clinicians in emergency departments in improving the management of prehospital emergency care, emergency radiology, triage, emergency department workflow, complex diagnoses, treatment, clinical decision-making, pediatric emergency care, trauma care, and the prediction of admissions, discharges, complications, and outcomes. However, the majority of these applications have been reported in retrospective studies, whereas randomized controlled trials (RCTs) are essential to determine the true value of AI in emergency settings. These applications can serve as effective tools in emergency departments when they are continuously supplied with high-quality real-time data and are adopted through collaboration between skilled data scientists and clinicians. Implementing these AI-assisted tools in emergency departments requires adequate infrastructure and machine learning operation systems. Since emergency medicine involves various clinical decision-making scenarios based on classifications, flowcharts, and well-structured approaches, future well-designed prospective studies are necessary to achieve the goal of replacing conventional methods with new AI and machine learning techniques.

Introduction: Acute Kidney Injury (AKI) is a severe complication of vancomycin treatment due to its nephrotoxic effects. However, research on predicting AKI in this high-risk group remains limited. This study presents a stacking ensemble machine learning model designed to predict the onset of AKI in this patient population.

Methods: Leveraging data from 314 ICU patients, the model incorporates SHapley Additive exPlanations (SHAP) for enhanced interpretability, identifying key predictors such as serum creatinine levels, glucose variability, and patient age. The model achieved an Area Under the Curve (AUC) of 0.94, outperforming existing predictive approaches. By utilizing readily available clinical data and determining an optimal temporal prediction window, this model facilitates proactive clinical decision-making, aiming to reduce the risk of AKI and improve patient outcomes.

Results: The stacking ensemble model achieved 92% accuracy, 93% precision, 92% sensitivity, and 0.94 AUC in 314 ICU patients, pinpointing creatinine, glucose variability, and age as critical AKI predictors.

Conclusion: The findings suggest that integrating advanced machine learning techniques with interpretable artificial intelligence (AI) can provide a scalable and cost-effective solution for early AKI detection in diverse healthcare settings.

Introduction: While urine culture is the gold standard for the urinary tract infection (UTI) diagnosis, delays in results highlight the need for rapid tests. This study aimed to evaluate the accuracy of urine Gram staining, leukocyte esterase, and nitrite in predicting the presence of UTI.

Methods: A cross-sectional diagnostic accuracy study was conducted on adult patients undergoing urine culture at a high-complexity hospital in northeastern Colombia. The results of Gram staining and urinalysis (nitrite and leukocyte esterase) were compared to urine culture as the gold standard test, and screening performance characteristics were calculated and reported for individual and combined tests.

Results: A total of 2,123 urine cultures were analyzed, with 49.8% testing positive. Escherichia coli was the most common pathogen (24.7%), and 76.17% of patients received antibiotics, primarily ceftriaxone (38.7%). Gram staining showed 56.9% (95% confidence interval (CI)=54.4 to 59.4) sensitivity and 76.8% (95% CI=72.6 to 80.5) specificity, leukocyte esterase had 67.9% (95% CI= 65.3 to 70.4) sensitivity and 84.5% (95% CI=81.4 to 87.2) specificity, and nitrite demonstrated the highest sensitivity (85.3%, 95% CI=82.0 to 88.2). The combination of Gram staining (+), leukocyte esterase (+), and nitrite (+) achieved 87.6% (95% CI=84.2 to 90.5) sensitivity and 94.6% (95% CI=93.1 to 95.9) negative predictive value (NPV), with the decision tree identifying this combination as having the highest diagnostic utility (positive likelihood ratio (PLR) = 23.77, 95% CI=18.34 to 30.80).

Conclusions: It seems that, integrating urine Gram staining with leucocyte esterase and nitrite improves UTI diagnosis in high-complexity emergency settings, reducing unnecessary urine cultures and antibiotic use while enhancing resource utilization and mitigating bacterial resistance.

Introduction: Code stroke is a framework to reduce time and improve the quality of care in the prehospital setting. However, increased scene time, delays, and other barriers in the prehospital setting necessitate updating the current protocol. This study aimed to update the Iranian national code stroke protocol for the prehospital setting.

Methods: This study represents the results of the second phase of the Iranian Comprehensive Stroke Code Management Program, a mixed methods study. We used the Caspian scientific 10-step method to update this protocol, which included a literature review, critical appraisal, extraction of recommendations, face-content validity, the Delphi method, RAND method, expert panel, stakeholders, and publishing and printing. We divided the updated protocol into three stages (on scene, ambulance care, and on admission).

Results: Twenty experts (55% nurses; mean age 40.7±9.1 years, experience 15.9±7.9 years) were enrolled. On-Scene focuses on rapid ABC (airway, breathing, circulation) assessment, BEFAST (balance, eyes, face, arm, speech, and time) criteria, blood glucose check, and on-scene time under 5 minutes. Ambulance Care Involving SAMPLER (Symptoms, Allergies, Medications, Past medical history, Last time the patient was seen normally, Events leading up to the emergency medical service call, and Risk factor) history-taking, maintaining oxygen saturation ≥94%, symptom/witness documentation, electrocardiography (ECG) for cardiac-stroke cases, master's degree (MSN)-led transport coordination, and neurology team alerts and in-hospital admission ensuring precise handover, 724 pager alerts, stroke code clocks, computed tomography (CT)-ready team, and protocol updates via joint committees.

Conclusion: The main points were the stroke clock, pager 724, direct delivery to computed tomography scan, administering BEFAST, and reducing scene time. We recommend that each center to enhance the infrastructure and resources for implementation of these updates. In the next phase, we will implement and evaluate this protocol.

Introduction: ChatGPT, a general-purpose language model, is not specifically optimized for medical applications. This study aimed to assess the performance of ChatGPT-4 and o1-preview in generating differential diagnoses for common cases of ankle pain in emergency settings.

Methods: Common presentations of ankle pain were identified through consultations with an experienced orthopedic surgeon and a review of relevant hospital and social media sources. To replicate typical patient inquiries, questions were crafted in simple, non-technical language, requesting three possible differential diagnoses for each scenario. The second phase involved designing case vignettes reflecting scenarios typical for triage nurses or physicians. Responses from ChatGPT were evaluated against a benchmark established by two experienced orthopedic surgeons, with a scoring system assessing the accuracy, clarity, and relevance of the differential diagnoses based on their order.

Results: In 21 ankle pain presentations, ChatGPT-o1 preview outperformed ChatGPT-4 in both accuracy and clarity, with only the clarity score reaching statistical significance (p < 0.001). ChatGPT-o1 preview also had a significantly higher total score (p = 0.004). In 15 case vignettes, ChatGPT-o1 preview scored better on diagnostic and management clarity, though differences in diagnostic accuracy were not statistically significant. Among 51 questions, ChatGPT-4 and ChatGPT-o1 preview produced incorrect responses for 5 (9.8%) and 4 (7.8%) questions, respectively. Inter-rater reliability analysis demonstrated excellent reliability of the scoring system with interclass coefficients of 0.99 (95% CI, 0.998-0.999) for accuracy scores and 0.99 (95% CI, 0.990-0.995) for clarity scores.

Conclusion: Our findings demonstrated that both ChatGPT-4 and ChatGPT-o1 preview provide acceptable performance in the triage of ankle pain cases in emergency settings. ChatGPT-o1 preview outperformed ChatGPT-4, offering clearer and more precise responses. While both models show potential as supportive tools, their role should remain supervised and strictly supplementary to clinical expertise.

Introduction: Angiogenesis through restoration of blood supply to the ischemic myocardium is a pivotal process that contributes to cardiac repair and leads to improvement of myocardial function. This study was conducted to evaluate cardioprotective effects of Diosgenin against myocardial infarction (MI) with focus on angiogenesis, myocardial fibrosis, and oxidative stress.

Methods: 4 groups of male Wistar rats were considered for this study: (1) sham, (2) MI, (3) MI+Vehicle and (4) MI+Diosgenin. MI model was created by occluding left anterior descending (LAD) artery for 30 minutes and reperfusion was established for 14 days by opening this artery. Diosgenin (50 mg/kg) was given orally to the rats for 21 days (from 7 days before MI induction until the end of the 14-day reperfusion period). Cardiac injury markers including troponin I, creatine kinase-MB (CK-MB), and lactate dehydrogenase (LDH) were measured using enzyme-linked immunosorbent assay (ELISA), same as cardiac stress oxidative markers (superoxide dismutase (SOD), Malondialdehyde (MDA), reduced glutathione (GSH)). Echocardiography was used to measure heart function parameters and myocardial fibrosis was assessed via a specific tissue staining named Masson׳s trichrome. Blood vessel staining kit was used to assess left ventricular angiogenesis.

Results: Ischemia-reperfusion injury increased serum levels of troponin I, CK-MB and LDH, as well as cardiac malondialdehyde (MDA) and myocardial fibrosis. MI also decreased myocardial function (Ejection fraction (EF)% and Fractional shortening (FS)%) and Diosgenin treatment reversed these parameters. Capillary density as marker of angiogenesis significantly increased in all of MI groups. However, development of angiogenesis was significantly higher in Diosgenin group compared with MI group.

Conclusion: Diosgenin exerts cardioprotective effects against ischemia-reperfusion injury by strengthening cardiac antioxidant defense and reducing deposition of collagen fibers. It seems that the strengthening of angiogenesis in heart tissue is one of the main mechanisms of Diosgenin to increase the heart's resistance against ischemia.