Statistical analyses
SIRS
REGRESSION
Variables with more than 20% missing values were excluded from further analysis. Random forest imputation was used to deal with missing data below 20%. Random forest imputation is a nonparametric algorithm that accommodates nonlinearities and interactions and does not require the specification of a specific parametric model [The least absolute shrinkage and selection operator (LASSO) (“LassoCV” method in Sklearn) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors and selected the best alpha = 0.0075 when one standard error of the minimum mean squared error (MSE) was used as a screening criterion. In order to select the optimal model from the 6 machine learning models, Delong’s test was used. Comparing the performance of the optimal machine learning model with LR, scoring systems (SOFA, qSOFA, SIRS, SAPS II, and BIASP). Clinical benefit was assessed using Decision Curve Analysis (DCA). A 