RESUMO
BACKGROUND: Although remimazolam is used as a general anesthetic in elderly patients due to its hemodynamic stability, the electroencephalogram (EEG) characteristics of remimazolam are not well-known. The purpose of this study was to identify the EEG features of remimazolam-induced unconsciousness in elderly patients and compare them with propofol. METHODS: Remimazolam (n=26) or propofol (n=26) were randomly administered for anesthesia induction in surgical patients. The hypnotic agent was blinded only to the patients. During the induction of anesthesia, remimazolam was administered at a rate of 6 mg/kg/h, and propofol was administered at a target effect-site concentration of 3.5 µg/ml. The EEG signals from 8 channels (Fp1,Fp2,Fz,F3,F4,Pz,P3,P4, referenced to A2, using the 10-20 system) were acquired during the induction of anesthesia and in the postoperative care unit. Power spectrum analysis was performed, and directed functional connectivity between frontal and parietal regions was evaluated using normalized symbolic transfer entropy. Functional connectivity in unconscious processes induced by remimazolam or propofol was compared with baseline. To compare each power of frequency over time of the two hypnotic agents, a permutation test with t statistic was conducted. RESULTS: Compared to the baseline in the alpha band, the feedback connectivity decreased by an average of 46% and 43%, respectively, after the loss of consciousness induced by remimazolam and propofol (95% CI for the mean difference:-0.073 to -0.044 for remimazolam, P<0.001,-0.068 to -0.042 for propofol,P<0.001). Asymmetry in the feedback and feedforward connectivity in the alpha band was suppressed after the loss of consciousness induced by remimazolam and propofol. There were no significant differences in the power of each frequency over time between the two hypnotic agents (minimum q-value=0.4235). CONCLUSIONS: Both regimens showed a greater decrease in feedback connectivity compared to a decrease in feedforward connectivity after loss of consciousness, leading to a disruption of asymmetry between the frontoparietal connectivity.
RESUMO
PURPOSE: The study aimed to compare the postoperative nausea and vomiting (PONV) preventive effect of repeated administration of ramosetron with the standard treatment group and compare models to predict the incidence of PONV using machine-learning techniques. METHODS: A total of 261 patients scheduled for breast surgery were analyzed to evaluate the effectiveness of repeated intravenous administration of ramosetron. All patients were administered 0.3 mg ramosetron just before the end of surgery. For the repeated dose of ramosetron group, an additional dose of 0.3 mg was administered at 4, 22, and 46 hours after the end of the surgery. Postoperative nausea, vomiting, and retching were evaluated using the Rhodes Index of Nausea, Vomiting, and Retching at 6, 24, and 48 hours postoperatively. Previously published randomized controlled data were combined with the data of this study to create a new dataset of 1390 patients, and machine-learning-based PONV prediction models (classification tree, random forest, extreme gradient boosting, and neural network) was constructed and compared with the Apfel model. FINDINGS: Fifty patients (38.5%) and 60 patients (45.8%) reported nausea, vomiting, or retching 48 hours postoperatively in the standard and repeated-dose groups, respectively (P = 0.317, χ2 test). Median sensitivity, specificity, and accuracy of the Apfel model analyzed using the training set were 0.815, 0.344, and 0.495, respectively. IMPLICATIONS: The repeated administration of ramosetron did not reduce the incidence of PONV. The Apfel model had high sensitivity, however, its specificity and accuracy were lower than that in machine-learning-based models.