Statistics
death
REGRESSION, EVENT
The baseline characteristics of the patients are reported as a distinct group. We presented continuous variables as means and standard deviations (SD) and categorical variables as percentages. We estimated the probability of in-hospital death using a random forest (RF) and multi-variable logistic regression (LR) model. All predicting variables were included in the "non-parsimonious model," regardless of their statistical significance or potential collinearity. Based on this model, each individual was assigned a risk score. Further receiver operating characteristic (ROC) curve analysis used this "estimator" as the test variable. To identify variables statistically associated with in-hospital death, a novel LR model was applied (stepwise selection, p < 0.05 for inclusion). We developed a parsimonious model using the newly selected variables partitioned as before with recalculated AUC of ROCs.Feature importance is the degree to which a feature (or a predicting factor) relates to the target outcome. The Gini importance calculated from the RF structure was used to identify the most relevant features among the predicting variables based on the mean decreased "impurity" [According to the select feature, survival analyses were performed regarding overall mortality up to 1 year, including Kaplan Meier curves, log-rank tests, and Cox regression models. In this study, the duration of ECMO was defined as beginning at the time of its placement and ending at the date of death (event), 1 year after the placement of ECMO, or termination of the study (censored).IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, N.Y. USA) was used for statistical analysis. Python 3.10.6 (Python Software Foundation, Beaverton, USA) and IBM SPSS Modeler trial version for Windows (IBM Corp., Armonk, N.Y. USA) were used to execute machine learning algorithms.