Resuscitative Endovascular Balloon Occlusion of the Aorta for Traumatic Cardiopulmonary Arrest in the Emergency Department: The First Case With Successful Return of Spontaneous Circulation in Taiwan.

Exsanguinating torso hemorrhage is a leading cause of death in trauma patients. Bleeding leads to hypothermia, acidosis, and coagulopathy, the so-called "lethal triad," and creates a vicious cycle. Therefore, bleeding control tops the priority list in the management of trauma patients. Placement of resuscitative endovascular balloon occlusion of the aorta (REBOA) in patients with traumatic non-compressible torso hemorrhage is a developing technique in the emergency departments (EDs) in Taiwan, and it is a possible solution for abdominal and pelvic trauma patients with hemodynamic instability. It not only temporarily controls bleeding below the inflation site but also increases cerebral and coronary circulation. It can create a bridge for definitive care such as an operation or an embolization, possibly preventing death. Compared to thoracotomy followed by an aortic cross clamp, REBOA is a less invasive and possibly, a more efficient way to control the hemorrhage and may lead to better overall survival. The use of REBOA has been proven to be associated with improved survival-to-discharge in severely injured trauma patients. We report a case of out-of-hospital cardiac arrest caused by penetrating injury wherein return of spontaneous circulation was successfully achieved after 39-minute cardiopulmonary resuscitation and REBOA placement in the ED. The REBOA balloon was deflated after bleeding was stopped during the laparotomy operation. The patient was then transferred to the intensive care unit for postoperative care. Unfortunately, the patient passed away approximately 12 hours after the surgery.

The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients.

Thalassemia and iron deficiency are the most common etiologies for microcytic anemia and there are indices discriminating both from common laboratory simple automatic counters. In this study a new classifier for discriminating thalassemia and non-thalassemia microcytic anemia was generated via combination of exciting indices with machine-learning techniques. A total of 350 Taiwanese adult patients whose anemia diagnosis, complete blood cell counts, and hemoglobin gene profiles were retrospectively reviewed. Thirteen prior established indices were applied to current cohort and the sensitivity, specificity, positive and negative predictive values were calculated. A support vector machine (SVM) with Monte-Carlo cross-validation procedure was adopted to generate the classifier. The performance of our classifier was compared with original indices by calculating the average classification error rate and area under the curve (AUC) for the sampled datasets. The performance of this SVM model showed average AUC of 0.76 and average error rate of 0.26, which surpassed all other indices. In conclusion, we developed a convenient tool for primary-care physicians when deferential diagnosis contains thalassemia for the Taiwanese adult population. This approach needs to be validated in other studies or bigger database.