Traumatic retropharyngeal hematoma is a rare condition and may be lethal in some cases. In patients with this condition, the absence of a vertebral fracture or a major vascular injury is extremely rare. We present the case of a 92-year-old man who hit his forehead by slipping on the floor in his house. He had no symptoms at the time; however, he experienced throat pain and dyspnea at 6 hours after the injury. On arrival, he complained of severe dyspnea; therefore, an emergency endotracheal intubation was performed. A lateral neck roentgenogram after intubation showed dilatation of the retropharyngeal and retrotracheal space and no evidence of a cervical vertebral fracture. Cervical computed tomography (CT) with contrast medium revealed a massive hematoma extending from the retropharyngeal to the superior mediastinal space but no evidence of contrast medium extravasation or a vertebral fracture. However, sagittal magnetic resonance imaging (MRI) revealed an anterior longitudinal ligament (C4-5 levels) injury. We determined that the cause of the hematoma was an anterior longitudinal ligament injury and a minor vascular injury around the injured ligament. Therefore, we recommend that patients with retropharyngeal hematoma undergo sagittal cervical MRI when roentgenography and CT reveal no evidence of injury.
Aged ; Aged, 80 and over ; Hematoma ; diagnosis ; etiology ; Humans ; Longitudinal Ligaments ; injuries ; Magnetic Resonance Imaging ; Male ; Pharyngeal Diseases ; diagnosis ; etiology ; Tomography, X-Ray Computed

We have developed and operated a browser-based questionnaire system for Kampo medicine based on conventional questionnaires and review of systems to reveal implicit Kampo wisdom both in patients' questionnaire data and in some Kampo specialists' examination data. However, the questionnaire data were found to be inaccurate because too many questions were included and cumbersome input steps were required. The purpose of the present study was to solve these problems and to develop a new patient-centered questionnaire system with fewer questions and an easier input method. After analyzing inquiry database from collaborating institutes and hospitals, we deleted, combined, and added questions. We changed the evaluation method of symptoms from a visual analogue scale to a simple staged evaluation, and introduced another method to evaluate the main symptoms in each time of visit using a visual analogue scale. At the same time, a tool for predicting Kampo pattern diagnoses based on the questionnaire data was implemented. We have already started collecting more accurate and reliable data using the new questionnaire system. It is expected to support routine practices and facilitate more precise clinical research on Kampo medicine.

We have previously reported on a predictive model for deficiency-excess pattern diagnosis that was unable to predict the medium pattern. In this study, we aimed to develop predictive models for deficiency, medium,and excess pattern diagnosis, and to confirm whether cutoff values for diagnosis differed between the clinics. We collected data from patients' first visit to one of six Kampo clinics in Japan from January 2012 to February 2015. Exclusion criteria included unwillingness to participate in the study, missing data, duplicate data, under 20 years old, 20 or less subjective symptoms, and irrelevant patterns. In total, 1,068 participants were included. Participants were surveyed using a 153-item questionnaire. We constructed a predictive model for deficiency, medium, and excess pattern diagnosis using a random forest algorithm from training data, and extracted the most important items. We calculated predictive values for each participant by applying their data to the predictive model, and created receiver operating characteristic (ROC) curves with excess-medium and medium-deficiency patterns. Furthermore, we calculated the cutoff value for these patterns in each clinic using ROC curves, and compared them. Body mass index and blood pressure were the most important items. In all clinics, the cutoff values for diagnosis of excess-medium and medium-deficiency patterns was > 0.5 and < 0.5, respectively. We created a predictive model for deficiency, medium, and excess pattern diagnosis from the data of six Kampo clinics in Japan. The cutoff values for these patterns fell within a narrow range in the six clinics.