Statistical analysis
PONV, nausea and vomiting
EVENT, DIABETES MELLITUS, REGRESSION, MOTION SICKNESS, HYPERTENSION
All analyses were performed using R software (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS Statistics for Windows (version 21.0, SPSS Inc. Chicago, Illinois, USA). Statistical significance was defined by a two-sided Based on clinical importance, scientific knowledge, previous research, and the actual data that could be collected for the study, we collected data from electronic medical records, which included age, sex, body mass index (BMI), smoking status, history of diabetes mellitus, hypertension, motion sickness and PONV, simplified Apfel score, type of surgery, duration of anesthesia and operation, total fluid input, use of intraoperative opioids, sugammadex, penehyclidine and rescue opioids. Univariate analysis and multiple logistic regression models were used to determine the influence of each variable on PONV. Variables were initially assessed in the univariate analysis, and the variables found significantly (To minimize possible selection bias between the sugammadex and neostigmine groups, we performed a PSM and inverse probability of treatment weighting (IPTW) method to control for observed confounding and verify the comparative results between the compared groups. First, we predicted the probability of receiving sugammadex for each patient using a logistic regression model by including all potential confounders as covariates. Second, we used the estimated propensity scores as weights for patients who received sugammadex and the inverse of 1 minus the propensity score for patients who received neostigmine, which generated a weighted cohort. Patients who received sugammadex were matched on a 1:1 ratio with patients who received neostigmine using nearest neighbor method within a caliper width of 0.1 of the standard deviation without replacement to yield the well matching results. Afterward, unadjusted differences between the sugammadex and neostigmine groups were characterized using the standardized mean difference. Absolute standardized mean difference (ASD) values above 0.2 were deemed to represent meaningful covariate imbalance. Following IPTW, we used conditional logistic regression analysis to compare clinical outcomes to assess the association between sugammadex and any event of nausea and vomiting, with results expressed as adjusted odds ratio (OR) with 95% confidence intervals (CIs) for categorical data and mean difference (MD) for continuous data. The cumulative incidence of the first passage of flatus was estimated using the Kaplan–Meier method and compared using the log-rank test. We also performed a subgroup analysis to compare the two groups within the first 24 h and 24–48 h after LBS.Because this is a post-hoc analysis and we have utilized all available patients who met our inclusion and exclusion criteria, we performed a power calculation. We considered it clinically meaningful if logistic regression showed sugammadex had an OR for PONV less than 0.55 compared with receiving neostigmine. Assuming type I error of 0.05 and effective sample sizes of 400 observations after applying IPTW with a 1:1 ratio, we confirmed that the statistical power was 0.82. The sample size justification was performed using Power Analysis & Sample Size software (PASS, version 15.0. NCSS, LLC. Kaysville, Utah, USA).