Superior mesenteric artery (SMA) aneurysms (SMAAs) are rare and account for approximately 7% of all visceral artery aneurysms. If the anatomical complexity permits and the patency of organ perfusion is allowed, then an endovascular approach is the first choice for minimally invasive procedures. We report the case of a 92-year-old female with a giant SMAA and challenging anatomy, including a short proximal sealing zone from the origin of the SMA and a short distal sealing zone from the hepatic artery bifurcation. In view of her advanced age, she was treated endovascularly with covered stents. Reintervention was required to correct a postoperative endoleak; however, a favorable outcome was achieved with endovascular therapy.

Objective: In this study, we analyzed the Japanese Adverse Drug Event Report (JADER) database in order detect unexpected adverse events using three polypharmacy machine learning models.Methods: The patient’s age, weight, height, gender, date and time of onset, subsequent appearance, and the taking medicines were preprocessed. They were applied for the prediction of adverse events using three machine learning procedures such as support vector machine (SVM), deep neural network (DNN) and random forest (RF).Results: Precision, matching, reproduction and F-values were almost same between the three techniques. Polypharmacy effects were predicted in approximately 80% of adverse events. Unexpected predictions were observed between DNN and RF, but different from SVM.Conclusion: Results suggest that the combination of DNN or RF and SVM can yield accurate predictions. We also suggest that RF is more useful because of its easy validation.