PurposeGlufosinate poisoning can cause neurologic complications that may be difficult to treat due to delayed manifestation. Studies assessing possible predictors of complications are lacking. Although serum ammonia level is a potential predictor of severe neurotoxicity, it has only been assessed via case reports. Therefore, we investigated factors that predict neurologic complications in acute glufosinate-poisoned patients.Materials and MethodsWe conducted a retrospective review of 45 consecutive glufosinate-poisoning cases that were diagnosed in the emergency department (ED) of Wonju Severance Christian Hospital between May 2007 and July 2014. Patients with a Glasgow Coma Scale (GCS) score of <8, seizure, and/or amnesia were defined to a neurologic complication group.ResultsThe neurologic complication group (29 patients, 64.4%) comprised patients with GCS<8 (27 patients, 60.0%), seizure (23 patients, 51.1%), and amnesia (5 patients, 11.1%). Non-neurologic complications included respiratory failure (14 patients, 31.1%), intubation and ventilator care (23 patients, 51.1%), shock (2 patients, 4.4%), pneumonia (16 patients, 35.6%), acute kidney injury (10 patients, 22.2%), and death (4 patients, 8.9%). Complications of GCS<8, seizure, respiratory failure, and intubation and ventilator care appeared during latent periods within 11 hrs, 34 hrs, 14 hrs, and 48 hrs, respectively. Initial serum ammonia was a predictor of neurologic complications [odds ratio 1.039, 95% confidence interval (1.001-1.078), p=0.046 and area under the curve 0.742].ConclusionNeurologic complications developed in 64.4% of patients with acute glufosinate poisoning. The most common complication was GCS<8. Initial serum ammonia level, which can be readily assessed in the ED, was a predictor of neurologic complications.

Delayed onset of neuropsychiatric symptoms after apparent recovery from acute carbon monoxide (CO) poisoning has been described as delayed neuropsychiatric sequelae (DNS). No previous study has determined whether early use of diffusion-weighted magnetic resonance imaging (DWI) can predict which patients will develop DNS in the acute CO poisoning. This retrospective observational study was performed on adult patients with acute CO poisoning consecutively treated over a 17-month period. All included patients with acute CO poisoning underwent DWI to evaluate brain injury within 72 h after CO exposure. DWI was evaluated as follows: (1) presence of pathology, (2) number of pathologies, (3) asymmetry, and (4) location of pathology. Patients were divided into two groups. The DNS group was composed of patients with delayed sequelae, while the non-DNS group included patients with no sequelae. A total of 102 patients with acute CO poisoning were finally enrolled in this study. DNS developed in 10 patients (9.8%). Between the DNS group and the non-DNS group, presence of pathology on DWI and initial Glasgow Coma Scale (GCS) showed significant difference. There was also a statistical difference between the non-DNS group and DNS group in terms of CO exposure time, troponin I, rhabdomyolysis, acute kidney injury, and pneumonia. The presence of pathology in DWI and initial GCS (cutoff: <12) at the emergency department served as an early predictors of DNS.

Background: The problem that is central to trauma research is the prediction of survival rate after trauma. Trauma and Injury Severity Score is being used for predicting survival rate after trauma. Many countries have conducted a study on the classification, characteristics of variables, and the validity of the Trauma and Injury Severity Score model. However, few investigations have been made on the characteristics of coefficients or variables related to Trauma and Injury Severity Score in Korea. Objectives: There is a need for coefficient analysis of Trauma and Injury Severity Score which was created based on the United States database to be optimized for the situation in Korea. Methods: This study examined how the currently used Trauma and Injury Severity Score coefficients were developed and created for trauma patients visiting the emergency department in a hospital in Korea using the analytical method. A total of 34,340 trauma patients who were hospitalized into an emergency center from January 2012 to December 2014 for 3 years were analyzed with trauma registry established on August 2006. Results: Trauma and Injury Severity Score coefficients were transformed with the methods that were used to make the existing Trauma and Injury Severity Score coefficients using the trauma patients' data. Regression coefficients (B) were drawn by building up a logistic regression analysis model that used variables such as Injury Severity Score, Revised Trauma Score, and age depending on survival with Trauma and Injury Severity Score. Conclusion: With regard to Trauma and Injury Severity Score established in the United States differing from Korea in injury types, it seems possible to realize significant survival rate by deriving coefficients with data in Korea and reanalyzing them.

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Ankle fractures are common and, compared to other injuries, tend to be overlooked in the emergency department. We aim to develop a deep learning algorithm that can detect not only definite fractures but also obscure fractures. We collected the data of 1226 patients with suspected ankle fractures and performed both X-rays and CT scans. With anteroposterior (AP) and lateral ankle X-rays of 1040 patients with fractures and 186 normal patients, we developed a deep learning model. The training, validation, and test datasets were split in a 3/1/1 ratio. Data augmentation and under-sampling techniques were administered as part of the preprocessing. The Inception V3 model was utilized for the image classification. Performance of the model was validated using a confusion matrix and the area under the receiver operating characteristic curve (AUC-ROC). For the AP and lateral trials, the best accuracy and AUC values were 83%/0.91 in AP and 90%/0.95 in lateral. Additionally, the mean accuracy and AUC values were 83%/0.89 for the AP trials and 83%/0.9 for the lateral trials. The reliable dataset resulted in the CNN model providing higher accuracy than in past studies.