Journal of Anesthesia最新文献

Purpose: To compare patient-controlled epidural analgesia (PCEA) and epidural morphine (EM) for post-cesarean section analgesia in real-world experience from China.

Methods: Parturients receiving one dose of EM (1-2 mg), PCEA, or both EM and PCEA from Peking Union Medical College Hospital were retrospectively recruited. Logistic models were used to identify risk factors.

Results: Of 1079 parturients enrolled, 919 (85.2%) parturients received only EM, 105 (9.7%) parturients received PCEA, and 55 (5.1%) parturients received both EM and PCEA. Significantly more parturients from EM group requested supplementary analgesia than those from PCEA and PCEA + EM group (583, 63.4% vs 52, 49.5% vs 25, 45.5%, P = 0.001) with more times of supplementary analgesia (1, IQR: 0-2 vs 0, IQR: 0-1 vs 0, IQR: 0-1 times, P < 0.001) and larger amounts of nonsteroidal anti-inflammatory drugs (NSAIDs) (50, IQR: 0-100 mg vs 0, IQR: 0-50 mg vs 0, IQR: 0-50 mg, P < 0.001). In multivariable Logistic regression for the supplementary analgesia risk, the application of PCEA (OR: 0.557, 95%CI 0.396-0.783, P = 0.001) and the use of NSAIDs intraoperatively (OR: 2.996, 95%CI 1.811-4.957, P < 0.001) were identified as independent predictors. A total of 1040 (96.4%) patients received prophylactic antiemetic therapy during surgery. Only 13 (1.2%) and 7 (0.6%) patients in our cohort requested antiemetic and antipruritic drugs, respectively.

Conclusion: The use of PCEA was an independent protective factor for supplementary analgesia during the post-cesarean section. Prophylactic antiemetic therapy may reduce the side effects of post-cesarean analgesia.

Purpose: To assess the importance of appropriate opioid administration methods according to nociceptive monitoring.

Methods: We conducted a randomized controlled trial involving 54 patients who underwent robot-assisted laparoscopic radical prostatectomy at our hospital. Patients were randomly allocated to either receive nociception level (NOL)-directed intraoperative opioid management with a minimum flow of remifentanil (NOL group) or conventional intraoperative analgesic management (control group). The primary outcome was the mean intraoperative remifentanil infusion flow rate (intraoperative remifentanil usage [μg]/ideal body weight [kg]/operation time [min]). The main secondary outcomes were plasma concentrations of three perioperative inflammatory biomarkers (interleukin-6, C-reactive protein [CRP], and cortisol levels) and postoperative pain (Numeric Rating Scale [NRS]) scores 2 h postoperatively and on postoperative days 1, 2, 3, and 7.

Results: Compared with standard analgesia management, NOL-directed analgesic management reduced remifentanil consumption by 20% ( - 0.038; 95% confidence interval, - 0.059 to - 0.017; p = 0.0007). NOL-directed management did not lead to an increase in IL-6, CRP, or cortisol levels compared with conventional analgesic management. Furthermore, this protocol led to improvements in the NRS scores at rest 2 h postoperatively and upon movement up to postoperative day 3.

Conclusion: NOL-directed analgesic management reduced remifentanil consumption by 20% and the NRS scores at rest 2 h postoperatively and upon movement up to postoperative day 3 without an increase in inflammatory marker levels.

Registry number: Japan Registry of Clinical Trials, JRCTs052220034.

In this research methods tutorial of clinical anesthesia, we will explore techniques to estimate the influence of a myriad of factors on patient outcomes. Big data that contain information on patients, treated by individual anesthesiologists and surgical teams, at different hospitals, have an inherent multi-level data structure (Fig. 1). While researchers often attempt to determine the association between patient factors and outcomes, that does not provide clinicians with the whole story. Patient care is clustered together according to clinicians and hospitals where they receive treatment. Therefore, multi-level regression models are needed to validly estimate the influence of each factor at each level. In addition, we will explore how to estimate the influence that variability—for example, one anesthesiologist deciding to do one thing, while another takes a different approach—has on outcomes for patients, using the intra-class correlation coefficient for continuous outcomes and the median odds ratio for binary outcomes. From this tutorial, you should acquire a clearer understanding of how to perform and interpret multi-level regression modeling and estimate the influence of variable clinical practices on patient outcomes in order to answer common but complex clinical questions.

The purpose of this study is to investigate whether preoperative intubation simulation using custom-made simulator is useful during anesthesia induction for the children who have difficult airway. We included the children under 15 years of age who have difficult airway which had been already known. Prior to the scheduled surgery, CT imaging was performed and a 3D reconstruction of the face from the chest was performed. Then custom-made airway simulator was made. We tried to intubate custom-made simulator of patients preoperatively. We planned how to intubate the patient for anesthesia induction from the result of intubation simulation. The findings of direct laryngoscopy were compared with the findings during intubation. Three patients were included in this study. It took up to 3 weeks to create a simulator, which was difficult due to time constraints to accommodate emergency surgeries. Simulation findings correlated well with findings during anesthesia induction. There were no cases of severe hypotension or hypoxia during induction of anesthesia with the planned intubation method. In conclusion, preoperative intubation simulation using custom-made simulator may be useful for the patients who have difficult airway.