Small Hepatic Hemangioma Leading to Life-Threatening Bleeding Following Blunt Abdominal Trauma: A Case Report

A male pedestrian in his 30’s was hit by a car and immediately taken to hospital by ambulance. On arrival, his blood pressure was 83/64 mmHg and his heart rate was 140 beats/min. Computed tomography showed extravasation of contrast medium from the lateral segment of the liver. Given the exacerbation caused by hypotension, an emergency laparotomy was performed. Temporary hemostasis was achieved by packing with gauze and a subsequent transcatheter arterial embolization. At the 2nd laparotomy, a small amount of active bleeding from the injury site was noted; therefore, an emergency lateral segmentectomy was performed. The postoperative course was uneventful and he was discharged home 8 days later. The pathology of the liver specimen revealed that a 2.5 cm hepatic hemangioma was the cause of bleeding. Traumatic rupture of hepatic hemangiomas is rare, and most reported cases are of giant hemangiomas. Our case demonstrated that even a small hemangioma can elicit life-threatening bleeding in blunt abdominal trauma.

Clinical application of the integrated multicenter discharge summary database.

We performed the multi-year project to collect discharge summary from multiple hospitals and made the big text database to build a common document vector space, and developed various applications. We extracted 243,907 discharge summaries from seven hospitals. There was a difference in term structure and number of terms between the hospitals, however the differences by disease were similar. We built the vector space using TF-IDF method. We performed a cross-match analysis of DPC selection among seven hospitals. About 80% cases were correctly matched. The use of model data of other hospitals reduced selection rate to around 10%; however, integrated model data from all hospitals restored the selection rate.

Construction of the integrated multicentre discharge summary database.

We started a multi-year project to collect discharge summaries from multiple hospitals and create a big text database to build a common document vector space, and develop various applications such as the autoselection of the disease. As the first step, we extracted discharge summary from two hospitals. Using a text mining method, we carried out a DPC selection. There was a difference in term structure and number of terms between the discharge summaries from both hospitals. Nevertheless, the selection rate of the disease is resembled closely.