Statistical analysis
hypoxemia, ’
REGRESSION, SECONDARY
For the intention to treat analysis, we analyzed the recorded ventilation data according to the treatment that subjects actually received. Continuous variables are presented as medians with interquartile ranges (IQR), and categorical variables with numbers and proportions. Continuous data are presented as medians (quartile 25% to quartile 75%), regardless of distribution, for consistency and to enhance readability.The proportion of breaths in the ‘optimal’, ‘acceptable’ and ‘critical’ zones is summarized per subject according to the following equation:
Data for the primary outcome are presented as median (quartile 25% to quartile 75%) and mean ± standard deviation, and compared as a mean ratio (A one–sided All analysis for secondary endpoints tested for superiority and were two–sided. For secondary outcomes assessing the proportion of breaths and the incidence of hypoxemia, the denominator was the total number of breaths. The proportion of breaths in acceptable and critical zones, or with hypoxemia were analyzed using the same strategy as the primary outcome. For all other secondary endpoints, effect estimates were calculated using univariate logistic or linear regression, for binary and continuous endpoints respectively.In creating the per protocol analysis, we excluded subjects who had one or more major protocol violations, i.e., not meeting minimum ventilation time requirement, or receiving ventilation with the capnography sensor that they were not assigned to.Treatment effect on the primary outcome was analyzed according to following subgroups: 1) subjects who were successfully extubated before or after median postoperative ventilation duration; 2) subjects with shorter or longer than median intraoperative ventilation duration; and 3) subjects with PaOFor the analyses of ventilation parameters over the first three hours of ventilation, all parameters were summarized as the mean of every 5 minutes until extubation or 180 minutes, whichever came first. Sidestream and mainstream groups were compared using mixed–effect longitudinal models with the subjects as random effect, the variable of interest as the dependent variable and the time of measurement, randomization group and an interaction of time and randomization group as fixed effects.The effects of the intervention on the duration of weaning, and the time until ICU and hospital discharge were assessed using Kaplan–Meier survival curves and a log rank test to calculate a two–sided All analyses were performed using R statistical software (version 4.1.0, R Core Team, 2016, Vienna, Austria), and a 