Gastric intramural hematoma is an uncommon condition that can cause acute abdominal pain,and in severe cases,lead to hemorrhagic shock. Without prompt intervention,it may lead to serious consequences or even life-threatening situations.
Malignant hyperthermia (MH) is a rare anesthetic emergency that presents as a hypermetabolic response in genetically susceptible individuals exposed to volatile anesthetics or succinylcholine,and MH is also defined as a pharmacogenetic disorder of sarcoplasmic reticulum disorder of skeletal muscle.
Diffuse alveolar hemorrhage (DAH) is a life-threatening clinical condition characterized by bleeding into alveolar spaces,resulting in diffuse pulmonary infiltrates,hemoptysis,and hypoxemic respiratory failure.
Thrombotic thrombocytopenic purpura (TTP) is a rare hematological disorder classically characterized by a triad of symptoms:thrombocytopenia,microangiopathic hemolytic anemia,and neurological impairment. The presence of fever and renal injury,together with the triad,constitute the less common but classic pentad. Secondary TTPs,particularly infection-associated TTPs,are even rarer.[1]Herein,we report a case of secondary TTP following a severe infection.
Cardiovascular (CV) manifestations of adrenal insufficiency have been previously documented through several case reports,with heart failure (HF) and dilated cardiomyopathy as the most frequently reported presentations. Accelerated coronary artery disease (CAD)has been associated with adrenal insufficiency,typically with abrupt discontinuation of exogenous glucocorticoid.This poses a challenge in distinguishing whether the accelerated CAD results from corticosteroid usage or the resultant insufficiency. We present a rare case in which multiple CV manifestations prompted the diagnosis of hypocortisolism in a patient with no prior glucocorticoid use,with complete resolution after appropriate adrenal insufficiency therapy.
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.
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.
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.
Skin ulcers appearing preceding the diagnoses of acute myeloid leukemia (AML) often serve as critical clues to underlying hematologic malignancies.Literature indicates that pyoderma gangrenosum (PG)is strongly associated with myeloid malignancies (e.g.,myelodysplastic syndrome[MDS]and AML).
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.
BACKGROUND: Traditional burn severity scores have limited accuracy in predicting mortality in burn patients with infection. This study aimed to develop an interpretative machine learning (ML) model to predict 60-day mortality in burn patients with suspected infection. METHODS: Data on burn patients with suspected infection were extracted from the Dryad database and divided into a training cohort (70%) and a test cohort (30%). Feature selection was conducted by combining the Boruta algorithm and least absolute shrinkage and selection operator (LASSO) regression. Twelve ML models were developed to predict 60-day mortality. Model robustness was evaluated in the training cohort, and the discrimination capacity was assessed in the test cohort. DeLong's test was performed to compare the area under the curve (AUC) between the optimal model and the traditional scores (abbreviated burn severity index [ABSI] and revised Baux [rBaux]). SHapley Additive exPlanations (SHAP) analysis was used for model interpretation. RESULTS: A total of 1,391 adult burn patients with suspected infections were included: training cohort (n=973), test cohort (n=418). The overall mortality was 23.7% (n=329). The percentage of total body surface area (%TBSA), Acute Physiology and Chronic Health Evaluation IV (APACHE IV) score, and age were identified as significant predictors of 60-day mortality among burn patients with suspected infections. CatBoost achieved a well-balanced performance and better ability than the ABSI and rBaux did. CONCLUSION: The ML model incorporating the APACHE IV score improved the predicting performance of 60-day mortality in burn patients with infection. Its high interpretability may facilitates its clinical application for In the future.
Trauma remains one of the leading causes of death worldwide and represents a major public health burden,particularly among young adults.[1,2]Traumatic injuries account for millions of deaths annually,with hemorrhage being one of the most preventable causes of trauma-related mortality.Penetrating polytrauma is frequently associated with severe organ injury,rapid blood loss,and high mortality.When penetrating polytrauma is complicated by hemorrhagic shock,the clinical situation becomes extremely critical and requires immediate hemorrhage control and rapid resuscitation.[3]
