Table 1.
Patient clinical characteristics stratified by entire sample, patients with and patients without perioperative respiratory adverse events. Data are presented as number/total number (%) of patients.
Table 2.
Number of patients with perioperative respiratory adverse event (PRAE) and their percentage from the entire sample and from the PRAE group.
Table 3.
Model composition and comparison. Multiple logistic regression in the development cohort. Model 1 includes only the hospital variable. Model 2 including hospital variables and anesthetic and surgical variables. In model 3, those without statistical significance were excluded. In the final model, in addition to all variables with statistical significance, we included the interaction between airway surgery and tracheal intubation.
Fig 1.
Receiver operating characteristic curves of the PEAR model, demonstrating AUROC 0,71.
Fig 2.
Calibration plot of observed vs. predicted perioperative respiratory adverse events for derivation cohort.
Markers represent the observed perioperative adverse event rate (position on the Y-axis) in relation to the predicted perioperative adverse event rate (position on the X-axis); error bars, 95% confidence interval for the observed complication rate; solid lines, perfect calibration (i.e., observed = predicted). The circles correspond to the proportion of patients at each risk level.
Fig 3.
Receiver operating characteristic curves of the PEAR model and the COLDS score, respectively demonstrating AUROC 0,71 and 0,63.