Objective: This study examined the up-to-date facts from real cases of emergency medical litigations to provide information to act appropriately in medical lawsuits against emergency medicine specialists. Methods: Data were collected from lawsuits from 2008 to 2020. Thirty-nine cases were collected and analyzed retrospectively. Six emergency medicine specialists and one resident with a lawyer’s license participated in the analysis. Results: Medical litigations have surged since 2015. The lawsuits were due mainly to misdiagnoses and violations of the duty to explain. Medical staff won in 12 (38.7%) cases and lost in 19 (61.3%). The main reason for losing was a violation of the duty of care. In criminal claims, 50% of cases resulted in confinement. The win rate was 1.4 times higher in cases where consultation was requested from other specialties (42.9% and 29.4% win with consultation and without, respectively). Conclusion The win rate in lawsuits is decreasing, and the level of penalty is becoming more severe. The decrease in the violation of duty to explain was attributed to education through the years. The win rate was higher when consulting with other specialists. Hence, active consultation is recommended. In addition, the system of radiology interpretation requires improvement because of the many misdiagnoses from reading diagnostic images.

Objective: This study examined the up-to-date facts from real cases of emergency medical litigations to provide information to act appropriately in medical lawsuits against emergency medicine specialists. Methods: Data were collected from lawsuits from 2008 to 2020. Thirty-nine cases were collected and analyzed retrospectively. Six emergency medicine specialists and one resident with a lawyer’s license participated in the analysis. Results: Medical litigations have surged since 2015. The lawsuits were due mainly to misdiagnoses and violations of the duty to explain. Medical staff won in 12 (38.7%) cases and lost in 19 (61.3%). The main reason for losing was a violation of the duty of care. In criminal claims, 50% of cases resulted in confinement. The win rate was 1.4 times higher in cases where consultation was requested from other specialties (42.9% and 29.4% win with consultation and without, respectively). Conclusion The win rate in lawsuits is decreasing, and the level of penalty is becoming more severe. The decrease in the violation of duty to explain was attributed to education through the years. The win rate was higher when consulting with other specialists. Hence, active consultation is recommended. In addition, the system of radiology interpretation requires improvement because of the many misdiagnoses from reading diagnostic images.

Objective: This study examined the up-to-date facts from real cases of emergency medical litigations to provide information to act appropriately in medical lawsuits against emergency medicine specialists. Methods: Data were collected from lawsuits from 2008 to 2020. Thirty-nine cases were collected and analyzed retrospectively. Six emergency medicine specialists and one resident with a lawyer’s license participated in the analysis. Results: Medical litigations have surged since 2015. The lawsuits were due mainly to misdiagnoses and violations of the duty to explain. Medical staff won in 12 (38.7%) cases and lost in 19 (61.3%). The main reason for losing was a violation of the duty of care. In criminal claims, 50% of cases resulted in confinement. The win rate was 1.4 times higher in cases where consultation was requested from other specialties (42.9% and 29.4% win with consultation and without, respectively). Conclusion The win rate in lawsuits is decreasing, and the level of penalty is becoming more severe. The decrease in the violation of duty to explain was attributed to education through the years. The win rate was higher when consulting with other specialists. Hence, active consultation is recommended. In addition, the system of radiology interpretation requires improvement because of the many misdiagnoses from reading diagnostic images.

Purpose: Electroencephalography (EEG) is useful for clarifying the association between cortical activity and cognitive processes in children. We investigated whether EEG abnormalities were correlated with developmental delay/intellectual disability (DD/ID) in the absence of clinical seizures. Methods: We retrospectively identified 166 children with DD/ID who underwent EEG at Pusan National University Hospital between January 2011 and December 2021. We compared clinical characteristics and test results between those with normal and those with abnormal EEGs. Additionally, we analyzed EEG abnormalities in relation to neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Results: Of the 166 patients, 39 (23.5%) displayed abnormal EEGs, while 127 (76.5%) had normal EEGs. Of the former, 25 (64.1%) patients exhibited epileptiform discharges, including 22 (56.3%) with focal and three (7.7%) with generalized discharges. Focal discharges most frequently affected the central area (35.9%). Twenty patients (51.3%) exhibited rhythmic slowing patterns. Epilepsy diagnoses were significantly more common among patients with abnormal EEGs (n=8, 20.5%) than among those with normal EEGs (n=9, 7.1%) (P<0.001). Of 22 patients with ASD, five (12.8%) had abnormal EEGs. Of 13 patients with ADHD, five (36.4%) had abnormal EEGs, all with epileptiform discharges. Two patients with ASD and two with ADHD exhibited rhythmic slowing. Abnormal EEG findings were significantly more common among those with genetic abnormalities compared to genetically normal patients (26 vs. 13, P=0.017). Conclusion EEG represents a potential screening tool for children with DD. Abnormal EEG findings are associated with increased epilepsy risk, informing diagnosis and treatment planning.

Objective: A wireless electroencephalography (EEG) headset was applied to patients with an unclear etiology of an altered mental status suspected of non-convulsive seizures in the emergency department. Methods: The study included patients who presented to the emergency department with an altered mental status from January 1, 2020, to April 30, 2023. The patients with abnormal findings in brain imaging or those with typical seizure movement were excluded. Real-time portable continuous EEG monitoring was conducted on patients with persistent altered consciousness despite receiving conservative treatments and showing improvement in vital signs and blood tests within 3 hours. The baseline characteristics, degree of consciousness alteration and neurological symptoms, initial clinical diagnosis, EEG findings, and medications used in cases where epileptiform discharges were examined. The clinical outcomes were analyzed retrospectively. Results: Among 26 patients, six patients showed epileptiform discharges. The final diagnoses included uremic encephalopathy (n=2), septic encephalopathy due to pneumonia (n=1), urinary tract infection (n=1), diabetic ketoacidosis (n=1), and mental change only (n=1). The associated symptoms were myoclonus in three patients and abnormal eye movements in two patients. Conclusion In cases where the causes of altered mental status remain unexplained despite appropriate evaluations and treatments, particularly when accompanied by myoclonus or abnormal eye movements, it is crucial to consider the possibility of non-convulsive seizures and promptly perform an EEG to differentiate and diagnose the underlying condition, even if initial tests and treatments have been performed.

Purpose: In the coronavirus disease 2019 pandemic, it is essential to supplement the changes in visiting patterns of individual emergency departments (EDs) to determine how to allocate emergency medicine resources. We compared the clinical features of children visiting the ED before and during the pandemic. Methods: Children younger than 18 years who visited the ED from February 2019 through December 2020, except January 2020, were enrolled, and divided into those who visited before and after January 2020 (the pre-pandemic and pandemic groups, respectively). We compared the 2 groups in terms of the baseline characteristics (age, sex, mode and route of arrival, cause of visit, and time of visit), chief complaint, ED diagnosis, initial acuity and its accuracy, and ED outcomes (length of stay and disposition). Results: The 31,036 children were categorized into the pre-pandemic (21,027 [67.8%]) and pandemic (10,009 [32.2%]) groups with a 52.4% decrease in the number of visits to the ED in the latter group. This decrease was more prominent in age 2-5 years (from 37.3% to 33.2%; P < 0.001), fever as a chief complaint (from 27.8% to 16.5%), diagnoses related to infection or the respiratory system (from 36.8% to 14.3%) or transfer to the ED (from 8.1% to 6.4%; P < 0.001). In contrast, increases were noted in age 12-17 years (from 14.9% to 17.4%; P < 0.001), injury (from 36.5% to 52.5%; P < 0.001), visits in the evening (from 54.9% to 57.4%; P < 0.001), length of stay longer than 6 hours (from 3.5% to 6.3%; P = 0.033), and low acuity (from 97.8% to 98.2%; P = 0.031). Conclusion The pandemic has brought about changes in visiting patterns of the ED. This study may help prepare strategies for the appropriate allocation and deployment of emergency medicine resources in the pandemic era.

Objective: Recent studies have suggested that deep-learning models can satisfactorily assist in fracture diagnosis. We aimed to evaluate the performance of two of such models in wrist fracture detection. Methods: We collected image data of patients who visited with wrist trauma at the emergency department. A dataset extracted from January 2018 to May 2020 was split into training (90%) and test (10%) datasets, and two types of convolutional neural networks (i.e., DenseNet-161 and ResNet-152) were trained to detect wrist fractures. Gradient-weighted class activation mapping was used to highlight the regions of radiograph scans that contributed to the decision of the model. Performance of the convolutional neural network models was evaluated using the area under the receiver operating characteristic curve. Results: For model training, we used 4,551 radiographs from 798 patients and 4,443 radiographs from 1,481 patients with and without fractures, respectively. The remaining 10% (300 radiographs from 100 patients with fractures and 690 radiographs from 230 patients without fractures) was used as a test dataset. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DenseNet-161 and ResNet-152 in the test dataset were 90.3%, 90.3%, 80.3%, 95.6%, and 90.3% and 88.6%, 88.4%, 76.9%, 94.7%, and 88.5%, respectively. The area under the receiver operating characteristic curves of DenseNet-161 and ResNet-152 for wrist fracture detection were 0.962 and 0.947, respectively. Conclusion We demonstrated that DenseNet-161 and ResNet-152 models could help detect wrist fractures in the emergency room with satisfactory performance.

Objective: This study aimed to clarify the relative prognostic value of each History, Electrocardiography, Age, Risk Factors, and Troponin (HEART) score component for major adverse cardiac events (MACE) within 3 months and validate the modified HEART (mHEART) score. Methods: This study evaluated the HEART score components for patients with chest symptoms visiting the emergency department from November 19, 2018 to November 19, 2019. All components were evaluated using logistic regression analysis and the scores for HEART, mHEART, and Thrombolysis in Myocardial Infarction (TIMI) were determined using the receiver operating characteristics curve. Results: The patients were divided into a derivation (809 patients) and a validation group (298 patients). In multivariate analysis, age did not show statistical significance in the detection of MACE within 3 months and the mHEART score was calculated after omitting the age component. The areas under the receiver operating characteristics curves for HEART, mHEART and TIMI scores in the prediction of MACE within 3 months were 0.88, 0.91, and 0.83, respectively, in the derivation group; and 0.88, 0.91, and 0.81, respectively, in the validation group. When the cutoff value for each scoring system was determined for the maintenance of a negative predictive value for a MACE rate >99%, the mHEART score showed the highest sensitivity, specificity, positive predictive value, and negative predictive value (97.4%, 54.2%, 23.7%, and 99.3%, respectively). Conclusion Our study showed that the mHEART score better detects short-term MACE in high-risk patients and ensures the safe disposition of low-risk patients than the HEART and TIMI scores.

Objective: Recent studies have suggested that deep-learning models can satisfactorily assist in fracture diagnosis. We aimed to evaluate the performance of two of such models in wrist fracture detection. Methods: We collected image data of patients who visited with wrist trauma at the emergency department. A dataset extracted from January 2018 to May 2020 was split into training (90%) and test (10%) datasets, and two types of convolutional neural networks (i.e., DenseNet-161 and ResNet-152) were trained to detect wrist fractures. Gradient-weighted class activation mapping was used to highlight the regions of radiograph scans that contributed to the decision of the model. Performance of the convolutional neural network models was evaluated using the area under the receiver operating characteristic curve. Results: For model training, we used 4,551 radiographs from 798 patients and 4,443 radiographs from 1,481 patients with and without fractures, respectively. The remaining 10% (300 radiographs from 100 patients with fractures and 690 radiographs from 230 patients without fractures) was used as a test dataset. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DenseNet-161 and ResNet-152 in the test dataset were 90.3%, 90.3%, 80.3%, 95.6%, and 90.3% and 88.6%, 88.4%, 76.9%, 94.7%, and 88.5%, respectively. The area under the receiver operating characteristic curves of DenseNet-161 and ResNet-152 for wrist fracture detection were 0.962 and 0.947, respectively. Conclusion We demonstrated that DenseNet-161 and ResNet-152 models could help detect wrist fractures in the emergency room with satisfactory performance.

Objective: We aimed to share our experience studying the effects and stability of intravenous propafenone in patients with atrial fibrillation (AF). Methods: This single-center retrospective study evaluated the baseline and clinical characteristics of patients with atrial fibrillation admitted to the emergency room and treated with propafenone between December 2018 and May 2019;patients were analyzed according to new onset AF and chronic AF groups. Results: Among 24 patients included in the present study, 15 patients were in the new onset AF group while nine were in the chronic AF group. Cardioversion was successful in 15 (73.3%) in the new onset AF group and two (22.2%) in the chronic AF group (P=0.033). The time to cardioversion was relatively short in patients in the new onset AF group (81 minutes vs. 122 minutes, log-rank, P=0.019). Recurrence of AF at 30 days was two (17.2%) in the new onset AF group and 0 (0.0%) in the chronic AF group. No major adverse event was observed except each hypotension in the new onset and chronic AF groups. Conclusion Sinus conversion of propafenone in patients with AF occurring within 48 hours in the emergency room may be considered.