Table of Content

01 July 2026, Volume 17 Issue 4

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Review Articles

Artificial intelligence in the emergency department-- applications, perceptions and limitations: a narrative review Open Access

Kamyab Pirouz, Vadym Shapovalov, Quincy K Tran, Sophie Gorup, Arman Hussain, Ali Pourmand

2026, 17(4):  301-311.  doi:10.5847/wjem.j.1920-8642.2026.064

Abstract    HTML    PDF (563KB)   

BACKGROUND Artificial intelligence (AI) is increasingly being integrated into emergency department (ED) workflows to assist with time-sensitive decision-making, documentation, diagnostics, and education. AI encompasses multiple computational approaches, including traditional machine learning (ML), deep learning (DL), and large language models (LLMs). We aim to provide insight into the current integration of AI into the multiple clinical spheres of the emergency medicine.

METHODS This narrative review analyzes available literature on the implementation of ML-based predictive systems, DL-based image and signal interpretation tools, and LLM-driven documentation and clinical reasoning support within emergency medicine.

RESULTS A comprehensive literature search was conducted across PubMed and SCOPUS from its inception to October 30, 2025. Overall, 189 articles were found, among them 51 were included in the final review. ML and DL models demonstrated strong performance in electrocardiogram interpretation, radiographic triage, and sepsis prediction, in some cases outperforming traditional clinical scoring tools. LLMs showed promise in documentation support, discharge summary generation, triage assistance, and educational applications; however, concerns remain regarding generalizability and clinical reliability. AI-assisted triage systems improved prioritization and time-to-provider metrics in selected settings but require further validation.

CONCLUSION AI holds substantial potential to augment emergency care delivery. Nevertheless, issues of transparency, bias, accountability, and human oversight remain critical. Current evidence supports AI as a clinical support tool rather than a replacement for physician judgment.

Comparison of predictive values of Acute Physiology and Chronic Health Evaluation scores for mortality in sepsis: a systematic review and meta-analysis Open Access

Ping Xu, Wei You, Shihao Liu, Cheng Zhang

2026, 17(4):  312-321.  doi:10.5847/wjem.j.1920-8642.2026.066

Abstract    HTML    PDF (772KB)   

BACKGROUND Acute Physiology and Chronic Health Evaluation (APACHE) scores are widely used in emergency medicine and critical care settings, yet their prognostic accuracy for sepsis-related mortality across different versions remains to be assessed thoroughly. This study aimed to compare the pooled prognostic performance of APACHE II, III, and IV in predicting in-hospital mortality in patients with sepsis.

METHODS We conducted a systematic review and meta-analysis by searching for articles in the PubMed, Embase, Web of Science, and Cochrane databases, with a publication date range from February 23, 2016, to June 8, 2024. Random effects models were employed to analyse pooled data, focusing on key metrics such as the area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR).

RESULTS A total of 33 articles were analyzed, 3 of which examined multiple APACHE scores. These articles collectively involved 60,041 patients: 25 articles with 9,619 patients assessed APACHE II, 4 articles with 32,862 patients assessed APACHE III, and 8 articles with 21,741 patients assessed APACHE IV. The AUC for in-hospital mortality improved progressively across versions: 0.75 (95% CI 0.66-0.83) for APACHE II, 0.81 (95% CI 0.73-0.89) for APACHE III, and 0.85 (95% CI: 0.78-0.92) for APACHE IV. APACHE IV showed numerically more favorable pooled prognostic estimates, with a DOR of 2.76 (95% CI 2.08-3.78), sensitivity of 0.78 (95% CI 0.66-0.86), specificity of 0.83 (95% CI 0.68-0.92), PLR of 4.29 (95% CI 2.33-7.89), and NLR of 0.28 (95% CI 0.17-0.46).

CONCLUSION APACHE IV demonstrated the superior prognostic accuracy to the other versions for sepsis-related mortality. With the increasing adoption of automated calculations through standardized software, APACHE IV might be incorporated into routine clinical workflows.

Original Articles

Improving mortality prediction in older adults with traumatic brain injury: incorporating neurological and imaging scores into Geriatric Trauma Outcome Score Open Access

Jonghee Han, Younghoon Sul, Hong Rye Kim

2026, 17(4):  322-329.  doi:10.5847/wjem.j.1920-8642.2026.073

Abstract    HTML    PDF (702KB)   

BACKGROUND Predicting mortality in elderly traumatic brain injury (TBI) patients is challenging. The Geriatric Trauma Outcome Score (GTOS) may underestimate risk in moderate to severe cases. We hypothesized that integrating the Glasgow Coma Scale (GCS) and the Rotterdam CT Score (RCS) into GTOS would enhance mortality prediction.

METHODS This retrospective cohort study included trauma patients aged ≥65 years with head Abbreviated Injury Scale (AIS) ≥3 and admission GCS ≤12, treated at a tertiary trauma center between January 2016 and December 2024. Three models were assessed: (1) GTOS only; (2) GTOS+GCS; and (3) GTOS+RCS, with GTOS logit as an offset for recalibration. Discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Brier score, calibration intercept, and slope. Internal validation of the models were performed using 200 bootstrap samples. Net clinical benefit was compared using decision curve analysis (DCA).

RESULTS A total of 171 patients were included, of whom 111 survived and 60 did not. The GTOS model showed poor performance (AUC=0.538, 95%CI 0.442-0.664; Brier score 0.292) of prediction of 30-day mortality. Adding GCS improved model performance (AUC=0.721, 95%CI 0.652-0.793; Brier score 0.209). The GTOS+RCS model showed superior results (AUC=0.883, 95%CI 0.843-0.934; Brier score 0.134), with the greatest net benefit in DCA.

CONCLUSION Integrating RCS into GTOS enhances mortality prediction accuracy and clinical utility for elderly TBI patients. The combined model demonstrates superior discrimination, calibration, and decision analytic benefit, suggesting the need for prospective validation in clinical settings

Emergency transfusion of uncross-matched O-type red blood cells in patients with severe pelvic fracture: a multi-center propensity score-weighted analysis

Hui Li, Yong Fu, Anyong Yu, Guangbin Huang, Yong Luo, Yunfeng Yi, Gongliang Du, Xingwen Zhang, Eryue Qiu, Cai Lin, Fei Xia, Dingyuan Du, Mao Zhang

2026, 17(4):  330-338.  doi:10.5847/wjem.j.1920-8642.2026.074

Abstract    HTML    PDF (511KB)   

BACKGROUND The transfusion of uncross-matched O-type red blood cells (URBCs) is the global standard of care for emergency resuscitation in trauma. However, its application in China remains limited due to concerns over safety and resource availability. This study aimed to evaluate the clinical efficacy and safety of URBCs in patients with severe pelvic fractures.

METHODS In this multicenter, retrospective cohort study (2019-2023), patients with severe pelvic fractures (Abbreviated Injury Scale [AIS]≥3) transfused within 24 h of admission were stratified into the URBC and non-URBC groups. Propensity score overlap weighting (PSOW) was used to address confounding factors. The primary outcome was 30-day in-hospital mortality. Secondary outcomes included length of stay (LOS), 24-hour blood product consumption, and in-hospital complications.

RESULTS A total of 526 patients were included (URBC group, n=57; non-URBC group, n=469). After PSOW adjustment, there was no significant difference in 30-day mortality (21.4% vs. 21.3%, P=0.989) between the URBC and non-URBC groups. However, the URBC group received significantly lower volumes of plasma, platelets, and cryoprecipitate within the first 24 h (all P<0.05). No clinically significant acute hemolytic transfusion reactions were observed during the study period. Furthermore, subgroup analysis of non-O-type blood recipients (57.1%) showed no significant differences in mortality compared with their O-type counterparts, and no acute hemolytic transfusion reactions were observed.

CONCLUSION Early administration of URBCs for severe pelvic fractures is safe. While not associated with increased mortality or hemolytic transfusion reactions, early use of URBCs was linked to a significant reduction in coagulation-related blood product consumption. This study provides evidence supporting the implementation of URBC within resource-constrained trauma setting to improve resuscitation efficiency.

Effects of external factors on red blood cell concentrate quality during helicopter transport: a pilot study Open Access

Daniel Staribacher, Astrid Voill-Glaninger, Christof Jungbauer, Robert Geretschläger, Tanja Panic-Jankovic, André Viveiros, Julia Zwiebler, Guenther C Feigl, Dzmitry Kuzmin

2026, 17(4):  339-343.  doi:10.5847/wjem.j.1920-8642.2026.067

Abstract    HTML    PDF (1358KB)   

BACKGROUND Prehospital red blood cell concentrate (RBC) transfusion is clinically advantageous and has an impact on all-cause mortality, especially in cases of major traumatic hemorrhage. The most frequent mean of prehospital transportation for blood and blood products is the helicopter. However, data on how external conditions during the helicopter flight affect the RBC quality are currently insufficient. There are also no data on how long the latter can be transported by helicopter without qualitative changes. This study aims to investigate how helicopter flight conditions affect RBC quality and to determine the maximum safe transport duration.

METHODS Four pairs of RBCs from different donors were taken. Each RBC pair was first mixed and homogenised, and then divided into two parts. The first was transported by helicopter for 22 successive days and the second served as control and was stored at 2 °C-6 °C. On days 1, 8, 15, and 22, samples of each RBC were taken, and hemolysis rates were calculated. A final sample was taken on day 36, which also marked the expiry date of the RBCs.

RESULTS The study maintained stable storage temperatures within the ideal range throughout helicopter transport. Despite variable conditions including number of starts/landings, flight time, outside temperatures, wind speed, and the cabin temperature, the hemolysis rates of transported RBCs did not differ significantly from those of control RBCs (median 0.061% vs. 0.066%, P>0.4). Furthermore, after 14 days of post-transport storage (day 36), hemolysis rates in both groups remained well within acceptable limits (0.151% vs. 0.133%).

CONCLUSION External conditions during helicopter transport for 22 d do not affect the RBC quality in terms of hemoglobin, hematocrit, free hemoglobin and hemolysis rate, rendering RBC transport and administration safe.

Development and validation of an interpretable machine learning model to predict high-risk patients with acute small bowel obstruction Open Access

Yingsong Zhao, Hongda Wang, Eryang Zhao, Guangyu Zhao, Junrong Zhang, Sanyang Chen, Yang Li, Yong Zhao, Wenfeng Zhuo, Guozheng Lyu, Ping Hu, Gang Zhao

2026, 17(4):  344-352.  doi:10.5847/wjem.j.1920-8642.2026.063

Abstract    HTML    PDF (928KB)   

BACKGROUND Small bowel obstruction (SBO) is a common emergency surgical disease for which identifying patients need emergency surgical resection due to nonviable small bowel tissue poses a significant challenge. This study aimed to develop and validate a predictive model to guide surgical decision-making using readily available clinical data.

METHODS A retrospective cohort study was conducted using data from Wuhan Union Hospital between 2017 and 2023 to establish the prediction model, with an external validation cohort from two other medical centers. Six machine learning algorithms (Logistic Regression [LR], Random Forest [RF], K Nearest-Neighbours [KNN], Multilayer Perceptron [MLP], Adaptive Boosting [AdaBoost] and eXtreme Gradient Boosting [XGBoost]) were employed, and the final model was based on LR classifier. Performance was evaluated with various metrics including AUC-ROC, accuracy, and decision curve analysis. SHapley Additive exPlanations (SHAP) method was used for model interpretation.

RESULTS High-risk and low-risk SBO groups differed significantly in clinical, laboratory, imaging, treatment, and outcome variables. Ten predictors were retained: white blood cell count, history of abdominal operation, platelet, albumin, neutrophil, spiral sign, acute bellyache, ascites, lymphocyte count, and rebound tenderness. After comparing the six algorithms, LR was selected because it showed the most consistent generalization and calibration. The final LR model achieved an AUC of 0.889 (95% CI 0.855-0.923) in the training data, a mean cross-validation AUC of 0.876 (95% CI 0.773-0.978), and an AUC of 0.873 (95% CI 0.818-0.929) in the independent test set. In the external validation cohort from the two other medical centers, the model achieved an AUC of 0.762 (95% CI 0.693-0.831).

CONCLUSION An interpretable machine learning model was developed and validated for prediction of high-risk SBO patients upon admission. The model may offer decision support for clinicians, aiding in risk stratification and guiding treatment strategies.

The prognostic value of CT-quantified visceral and subcutaneous fat areas in sepsis Open Access

Tiancao Dong, Dianyin Yang, Jin Liu, Qian Yang, Chunxue Wang, Dongyang Zhao, Sen Jiang, Xiandong Liu, Xiaohui Zhou, Lunxian Tang

2026, 17(4):  353-362.  doi:10.5847/wjem.j.1920-8642.2026.068

Abstract    HTML    PDF (620KB)   

BACKGROUND This study aims to investigate the prognostic value of computed tomography (CT)-quantified visceral fat area (VFA) and subcutaneous fat area (SFA) in sepsis.

METHODS This study comprised a retrospective cohort (n=347) between January 2022 and July 2024 and a prospective internal validation cohort (n=160) between August 2024 and February 2025, all enrolling sepsis patients defined by the Sepsis-3 criteria. Abdominal CT at the third lumbar vertebra (L3) was used for VFA and SFA quantification. Prognostic factors for 28-day mortality were identified through least absolute shrinkage and selection operator (LASSO) regression and multivariable analysis. A nomogram was developed and validated using discrimination, calibration, and decision curve analysis (DCA).

RESULTS The retrospective cohort demonstrated area under the curve (AUC) values of 0.74 (95% CI 0.66-0.82) (VFA) and 0.76 (95% CI 0.69-0.84) (SFA) for 28-day mortality prediction. The nomogram, incorporating VFA, SFA, lactate (LAC), total cholesterol (TC), age, septic shock, and procalcitonin (PCT), was found to outperform Sequential Organ Failure Assessment (SOFA) and Acute Physiology And Chronic Health Evaluation (APACHE) II scores with an AUC of 0.93 (95% CI 0.89-0.96) compared to 0.84 and 0.82.

CONCLUSION The CT-based nomogram integrating adiposity metrics provides a valuable prognostic tool for sepsis patients, though optimal adiposity ranges require further investigation.

Cognitive biases and misdiagnosis of aortic dissection at emergency departments Open Access

Francesco Salinaro, Santi Di Pietro, Alessandro Marenzi, Valeria Fumagalli, Benedetta Traversi, Debora Monti, Martina Seguenti, Stefano Perlini, Alessandra Mammana, Daniela Maria Taccagna, Silvia Damiana Visonà

2026, 17(4):  363-369.  doi:10.5847/wjem.j.1920-8642.2026.072

Abstract    HTML    PDF (502KB)   

BACKGROUND Aortic dissection (AD) is still a challenging diagnosis with poor prognosis, and its misdiagnosis remains an unresolved problem. Postmortem examination has long been deemed important for understanding the diseases and determining the cause of death. We aim to investigate the concordance between clinical diagnosis and autopsy findings and to identify the most common clinical “red flags” and the cognitive errors involved in the management of AD.

METHODS This retrospective, observational, single-centre study analysed a series of 30 forensic autopsies focusing on the 7 cases of AD. Clinical-autopsy discrepancies were categorised into six predefined classes, and cognitive biases were classified into four types. All records were independently reviewed by two emergency physicians and two forensic pathologists, with consultation from a cognitive psychologist and consensus reached through collegial discussion.

RESULTS These seven patients had a mean age of 59 years (range 48-71 years). Loss of consciousness (4/7 patients), lumbar pain (4/7), psychomotor agitation (4/7) and two or more ER visits were considered red flags for AD. The most common malperfusion syndromes were coronary and femoral-iliac. Among the types of diagnostic errors, faulty verification bias was the most common, followed by biases related to faulty information processing. In three patients (43%), the failure to diagnose AD directly contributed to death, as an earlier diagnosis would likely have altered clinical management and improved survival outcomes.

CONCLUSION There remains a significant unmet need to increase awareness and clinical suspicion of AD in patients presenting with atypical symptoms, non-traditional risk factors, or ambiguous physical findings. Furthermore, identifying and sharing the cognitive underpinnings of errors may aid in reducing misdiagnosis and optimizing both the timing and modality of clinical management.

Research Letters

Evolution of guidelines for the management of foreign body airway obstruction from 2005 to 2025 Open Access

Sijia Tian, Yuzhu Wang, Jinjun Zhang

2026, 17(4):  370-372.  doi:10.5847/wjem.j.1920-8642.2026.070

Abstract    HTML    PDF (343KB)   

A multicenter audit of case completeness and missing patients in a Middle East national trauma registry Open Access

Neda Al-Sadat Fatemi, Maryam Jahanbakhsh, Shahriar Zehtabchi, Mehdi Nasr Isfahani, Mohammad Reza Zafarghandi

2026, 17(4):  373-377.  doi:10.5847/wjem.j.1920-8642.2026.069

Abstract    HTML    PDF (426KB)   

Clinical characteristics and prognosis of organofluoride exposure-related lung injury: a case series Open Access

Lijuan Cheng, Ping Wang, Yuanqiang Lu

2026, 17(4):  378-381.  doi:10.5847/wjem.j.1920-8642.2026.071

Abstract    HTML    PDF (1001KB)   

Case Letters

Evans syndrome following influenza A presenting with diffuse alveolar hemorrhage managed with extracorporeal membrane oxygenation Open Access

Haotian Lu, Anke Shi, Jieqiong Yu, Jing Zhao, Guoqiang Zhang, Shengtao Yan

2026, 17(4):  382-384.  doi:10.5847/wjem.j.1920-8642.2026.056

Abstract    HTML    PDF (762KB)   

From skin ulceration to truth revealed: a case report of acute myeloid leukemia presenting primarily with cutaneous manifestations Open Access

Yuanyuan Luo, Huanhuan Gong, Ning Yang, Jie Wang, Qin Huang, Jinxing Ye, Xiangmin Li

2026, 17(4):  385-387.  doi:10.5847/wjem.j.1920-8642.2026.061

Abstract    HTML    PDF (891KB)   

Secondary thrombotic thrombocytopenic purpura following severe infection: a case report Open Access

Ni Yang, Longfei Li, Longfei Pan

2026, 17(4):  388-390.  doi:10.5847/wjem.j.1920-8642.2026.060

Abstract    HTML    PDF (586KB)   

Acute cardiovascular manifestations of hypocortisolism Open Access

Filipa Gerardo, Carolina Mateus, Mariana Passos, Miguel Santos, Daniel Faria, Inês Fialho, David Roque, João Bicho Augusto

2026, 17(4):  391-393.  doi:10.5847/wjem.j.1920-8642.2026.059

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Importance of the early recognition, early suspicion, and early treatment for malignant hyperthermia crisis: two case reports Open Access

Chuwen Hu, Wen Song, Huijun Lin, Yi Wu, Ganglan Fu, Daowei Lin

2026, 17(4):  394-396.  doi:10.5847/wjem.j.1920-8642.2026.058

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Ruptured gastric intramural hematoma presenting with shock: a case report Open Access

Xiali Qiu, Liheng Ma, Yuefu Zhan

2026, 17(4):  397-399.  doi:10.5847/wjem.j.1920-8642.2026.057

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Effect of hemoperfusion and hemofiltration in lead clearance: a case report Open Access

Jiaqi Xu, Qing Tang, Lina Jiao, Xin Luo, Songtao Shou, Lijun Wang

2026, 17(4):  400-402.  doi:10.5847/wjem.j.1920-8642.2026.062

Abstract    HTML    PDF (383KB)