Statistical analysis
Continuous variables were shown as mean ± standard deviation (SD) or median (interquartile range [IQR]), whichever was appropriate. Categorical variables were shown as numbers (percentage). To identify factors independently associated with escalated care, we used logistic regression and estimated odds ratios (ORs) and adjusted for potential confounding variables.
Initially, the regression model only included variables associated with escalated care with values ​​of p <0.2 or that changed the effect estimate by over 10% after their inclusion. A multivariate logistic regression model was performed with a backward elimination method, used with a p value of 0.05 as the limit value for the model entry. The variable selection and modeling processes were made following the recommendations of Greenland(12). The method of Sullivan et al. was used to generate the risk scores and estimate the risks observed with each score (13). To assess discrimination, area-under-the-curve (AUC) was estimated with a 95% confidence interval and plotted using AUC-ROC plots(14). To correct prediction probabilities for over-optimistic predictions and to evaluate the calibration, the model was analyzed by comparing the predicted probability to the observed probability of mortality and examined with a calibration plot and calibration slope with 95% CI. Calibration plots (STATA function: pmcalplot) displayed observed risk by deciles of the predicted risk and examined risk at the individual level using Locally Weighted Scatterplot Smoothing algorithms(15). We also calculated the Hosmer – Lemeshow goodness of fit test as well as calibration curves between the predicted probabilities and the observed data. To correct sampling bias of variance parameters and to evaluate the internal validity of the model, repeated curved validation ”tenfold cross-validation” were used, comparing the area under the ROC curve obtained in the repetitions with that observed in the model (15, 16); as well as with the bootstrapping technique with which both the adjusted oppressiveness and area values ​​were estimated. All statistical tests were two-tailed, and the significance level used was p  < 0.05. The data were analyzed with Statistical Package Stata 15.0 (Stata Corporation, College Station, TX).