Medcalc15

SPSS 17.0 software (IBM, Chicago, IL, USA) was used to evaluate the normality of the distribution of the dataset using the Kolmogorov-Smirnov test and to perform the chi-square test for the categorical data. The T test is used for normally distributed variables, and the Mann-Whitney test is used for non-normally distributed variables. The conditional forward stepwise selection method was applied for the multivariable logistic regression model. MedCalc15.8 software (MedCalc, Ostend, Belgium) was used to assess the ROC curves for the diagnostic performance of the models, and differences between the various AUCs were compared with the DeLong test. R statistical software was used for all other statistical analyses. The “mRMRe” and “gbm” packages were used for mRMR and GBDT analyses, respectively. DCA plots were generated with the “dca. R” package. Two-tailed p-values less than 0.05 were considered statistically significant.

SPSS 17.0 software (IBM, Chicago, IL, USA) was used to perform the Kolmogorov–Smirnov test for evaluating the normality of the distribution of the data, and the chi-square test for the categorical data. The likelihood ratio test with backward step-down selection was applied to the multivariate logistic regression model. VIFs were calculated using the SPSS 17.0 software. The MedCalc15.8 software (MedCalc, Ostend, Belgium) was used to assess the ROC curves, and differences between various AUCs were compared with the DeLong test. The R statistical software Version 3.4.1 was used for all other statistical analyses. The “mRMRe” and “glmnet” packages were used for mRMR and LASSO analyses. Calibration plots and the radiomics nomogram were established with the “rms” package, and DCA with the “dca.R” package. Two-sided p < 0.05 were considered as being statistically significant.

Statistical analysis was performed using SPSS version 20.0 for Windows (SPSS Inc., Chicago, IL, USA) and MedCalc 15.8 software (MedCalc, Ostend, Belgium). Data were analyzed, and the continuous variables were reported as mean ± standard deviation (SD), and nominal variables were reported as total number and percentages.
Variables were first evaluated by One-Sample Kolmogorov-Smirnov test as a normality test to choose the type of statistical tests –parametric or non-parametric test–, and the results showed that asymp. Sig. (2-tailed) levels ≤0.05 so we decided to use non-parametric tests. For statistical analysis, correlations between variables were evaluated for significance by using the Spearman’s rho test. Categorical variables were evaluated by the Mann-Whitney U test of contingency. In all analyses, a ‘p’ value of less than 0.05 was considered statistically significant.
Apart from this, we established a receiver operating characteristic (ROC) curve to evaluate the ability of LC-1 and LC-2 to predict 30-day mortality. In this analysis, ROC-Area Under Curve (AUC) was calculated to quantify the accuracy of the predictive model. AUC value > 0.75 was appraised as satisfactory, AUC value > 0.8 was appraised as well, and AUC value > 0.9 was appraised as very good.