Statistical analysis
We used IBM SPSS Statistics 26 for statistics and analysis, and the R4.1.2 software was used to construct and verify the CPMs. The R packages used in this study included: “Rms 6.3.0 (Nomograms, Calibration curve)”, “DescTools 0.99.46 (C-Index)”, “ROCit 2.1.1” (ROC analysis), “ResourceSelection 0.3.5” (Hosmer-Lemeshow test), “Rmda 1.6” (DCA analysis). In the randomized controlled trial, the independent sample t-test was used to compare the data between the two groups, and the change trend and difference of the two groups of research data were compared by repeated measurement analysis of variance. We used bootstrap resampling method, the ability of AUC and C-Index evaluation models to distinguish patients with severe CRF from patients with mild CRF. The accuracy of the model was evaluated with Hosmer Lemeshow goodness of fit test and Calibration calibration curve, and the clinical practicability of the model was evaluated with DCA curve analysis results, so as to complete the internal evaluation of the model. Finally we used the established prediction model for severe CRF of CC patients to establish the prediction probability for each patient in the validation group, and then draw ROC curve, Calibration calibration curve and DCA curve according to the prediction probability and actual probability to complete the validation of the model. Inspection level: α=0.05 (bilateral), P <0.05.
The formula of CPMs is Logit(P )=ln(P /1-P )=β01 X1+β2 X2+β3 X3+⋯+βi Xi.