Statistical analysis
death, NRI
REGRESSION
We used the Kolmogorov-Smirnov test to test the distribution of variables. Continuous normally distributed variables were represented by the mean ± standard deviation, while continuous nonnormal distribution were represented by the median (interquartile range). Categorical variables were expressed as numbers (percentages). If the variable conformed to normal distributions, the baseline characteristics among the two groups (survive group and died in hospital group) were compared using the unpaired student’s t test, and continuous variables with nonnormal distributions were compared by the Mann-Whitney U test. The differences of the ratio of survive between groups of categorical variables were evaluated by the chi-square test. When comparing the categorical variables between groups, if the theoretical frequency was less than 5, Fisher’s exact test was used for analysis.We used without replacement sampling (the sample function in R) to randomly select 60% of all patients as training set and the last 40% as validation set. The data of the training set were used to build the prediction model, and the validation set was used to verify the model.We used the least absolute shrinkage and selection operator (LASSO) to select the optimal predictive risk factors for in-hospital mortality after PCI. LASSO implements the selection of variables by applying the regularization process. A penalty term is added to the coefficient of the model on the ordinary linear square, and a more refined model is obtained by constructing the penalty function [The factors selected by LASSO regression were incorporated into the prediction model. The 95% confidence interval of the odds ratio (OR) value and the The nomogram was compared with the SOFA and SAPS II. The sensitivity and specificity of the three models were used to generate a receiver operating characteristic (ROC) curve of each model, and the area under the receiver operating characteristic curve (AUROC) was calculated. The AUROC values and 95% confidence intervals were used to compare the different prediction models to confirm which model had better prediction ability. The comparison method was “Delong”. In addition to the AUROC, we used the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) to compare the predictive value between the nomogram and the SOFA or SAPS II. The calibration of the nomogram was evaluated by plotting the calibration curve. Decision curve analysis (DCA) could be performed by quantifying the net benefit under different threshold probabilities. DCA was used to evaluate the net benefit of nomogram in predicting the in-hospital death of patients after PCI. In the DCA diagram, the probability of patient is in hospital death is Pi; when Pi reaches a certain threshold (Pt), it is positive and treatment taken. There will be benefits from treatment of patients, as well as harm from treatment of non-patients and loss (disadvantages) from patient non-treatment [.Statistical analysis was performed using R version 4.0.3. We used the ‘glmnet’ R package to implement the LASSO regression model and the ‘rms’ package for the mortality risk prediction nomogram.