BACKGROUND: Midazolam has been reported to have a spinally mediated antinociceptive effect. In this randomized, double-blind study, we evaluated whether a small dose of midazolam added to fentanyl-ropivacaine mixture for PCEA (patient controlled epidural analgesia) improves epidural analgesia in patients underwent elective subtotal gastrectomy. METHODS: Forty five patients, ASA physical status I and II, undergoing subtotal gastrectomy were randomly allocated to receive 0.2% ropivacaine mixed with fentanyl 4microg/ml or 0.2% ropivacaine mixed with fentanyl 4microg/ml and midazolam 0.2 mg/ml. The infusion rate was set to deliver 4 ml/hr of the study solution, with a bolus of 2 ml per demand and a 20 minutes lockout time. RESULTS: Infused volume (P < 0.05) and VAS scores (P < 0.05) was significantly lower in the patients receiving midazolam. However, there were no differences in requiring rescue analgesics, PONV (postoperative nausea and vomiting), sedation scores, urinary retention, and pruritus between groups. CONCLUSIONS: Small dose of midazolam could augment analgesia without adverse effects when added to thoracic epidural infusion of fentanyl and ropivacaine.
Amides ; Analgesia ; Analgesia, Epidural ; Analgesics ; Double-Blind Method ; Fentanyl ; Gastrectomy ; Humans ; Midazolam ; Nausea ; Postoperative Nausea and Vomiting ; Pruritus ; Urinary Retention

Purpose: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). Methods: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. Results: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. Conclusion Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.