Spss 23 statistical software

The BUN was divided into normal and elevated groups as mentioned before. While analysing BUN in different creatinine, age, and BNP groups, BUN was divided into <30, 30–39, and >40. Creatinine was divided into normal (≤1.5 mg/dL) and elevated (>1.5 mg/dL); age was divided into <80, 80–90, and >90 years; and BNP levels were divided into <400, 400–1999, and >2000 pg/mL.
Using bi‐variable logistic regression, we analysed the relationship between demographic data, BUN, other laboratory results, and overall mortality. All variables with P value < 0.05 were included in the multivariate analysis, using stepwise Cox regression.
We used a multivariate, stepwise logistic regression to identify variables with independent effect on mortality. The goodness of fit of the model was determined by Hosmer–Lemeshow test. The discrimination threshold was classified by area under a curve receiver operating characteristic (AUC ROC).
The correlation between the BUN levels and other laboratory test results was tested by the ordinal‐by‐ordinal Spearman correlation. The same statistical analysis was performed with creatinine and GFR as predictor of all the aforementioned outcomes.
The significance level for testing the statistical hypothesis was determined as P < 0.05. The data processing was done with SPSS statistical software 23, Chicago, Illinois.

Statistical analyses were performed using SPSS statistical software 23.0 (IBM Corp.). Continuous variables are presented as the mean ± standard deviation and categorical variables were expressed as n (%). The Kolmogorov-Smirnov test was used to check the data for normality of distribution. The t-test was employed to compare variables with a normal distribution. The nonparametric Mann-Whitney U-test was used for variables without a normal distribution, such as the ROM and VAS score. Differences in categorical variables between the two groups were assessed with Fisher's exact test. P<0.05 was considered to indicate statistical significance.