Comparing the effects of propofol and ketamine on the emergence agitation of male children undergoing circumcision

Background Separation from the family, prolonged hunger, inability to perceive the surgical procedure performed, and feeling pain are among the main reasons for agitation in young children. In operations like circumcision, in which all bodily integrity is disrupted and children cannot make sense of it and feel punished, this agitation increases. The aim of the present study was to compare the effects of propofol and ketamine on the emergence agitation (EA) in children undergoing circumcision. Result When the patients were taken to post-anesthesia care unit (PACU), no statistically significant difference was observed between propofol and ketamine groups in the Aono’s four-point scale at minute 0 (p = 0.073). In the 5th minute, it was higher in the ketamine group compared to the propofol group (p < 0.001). With Aono’s four-point scale, EA diagnosis is made in areas with 3 and 4 points. The average Aono’s four-point scale in the ketamine group at the 5th minute was 3.08 ± 1.02. Since the Modified Steward score was ≥ 6, the time taken was longer in the ketamine group compared to the propofol group (p < 0.001). Conclusion EA does not only occur in inhalational anesthetics, it is also seen with ketamine. In view of the fact that ketamine can cause EA in children, it should not be used alone in anesthesia. Propofol provides a safe anesthesia. Instead of inhalational anesthesia, where the type of surgery is suitable, anesthesia with propofol infusion should be applied. Further research is required to investigate EA.

Postoperative delirium prediction using machine learning models and preoperative electronic health record data

Background Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. Methods This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. Results POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. Conclusion Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

Recommendations for anesthetic management for intraoperative neuromodulation cases

This paper performs a review of current literature as well as uses our single-center experience to discuss pre-operative, intra-operative and, briefly, postoperative management for dorsal column stimulators (DCSs), dorsal root ganglion (DRG) stimulators, peripheral nerve stimulators (PNSs) and intrathecal pumps. Generally, pre-operative antibiotics are recommended with discontinuation within 24 h postoperatively. For dorsal column and DRG stimulation, monitored anesthesia care or general anesthesia with intra-operative neuromonitoring is recommended; for peripheral nerve stimulation and intrathecal pump implementation, monitored anesthesia care is preferred. There is little information on appropriate anesthetic management during these forms of neuromodulation. More research is necessary to articulate specific pre-operative, intra-operative and postoperative management guidelines and recommendations for dorsal column stimulator, DRG stimulation, PNS and intrathecal pump implantation.

The Generalizability of Patients’ Preferences and Concerns regarding Anesthesia Care for Cesarean Delivery: A Prospective Survey

Background. False assumptions regarding the generalizability of patients’ expectations and preferences across different demographic groups may contribute in part to the increased prevalence of negative peripartum outcomes seen among women of color. The intention of this study was to determine preferences and concerns regarding anesthesia care during cesarean delivery in a largely African-American population and to compare them to those obtained in a prior study conducted in a demographically distinct population. Methods. Women presenting for scheduled cesarean delivery or induction of labor completed a preoperative survey requesting demographic information and the opportunity to rank ten common potential anesthetic outcomes in relation to each other from most to least desirable. Participants were also asked about their biggest fear concerning their anesthetic and their preferences and expectations regarding degree of wakefulness, pain, and other adverse events. Those who underwent cesarean delivery were administered a briefer postoperative survey. We tabulated preference rankings and then compared demographic and outcome data to that obtained in a previous study with a demographically dissimilar population. Results. A total of 73 women completed the preoperative survey, and 64 took the postoperative survey. Pain during and after cesarean delivery was ranked as least desirable outcomes and fear of paralysis was respondents’ principal concern with neuraxial anesthesia. Postoperative concerns were similar to preoperative concerns and did not correlate with the frequency with which specific adverse outcomes occurred. These results were consistent with those from the previous study despite the women in this study being more likely to be younger, unmarried, African-American, and less educated than those in the previous investigation. Conclusions. Patient preference rankings and concerns were remarkably similar to those previously demonstrated despite a number of demographic differences between the two populations, suggesting generalizability of these preferences to a broader obstetric population.