Spss software version 22.0 for windows

Statistical analyses were performed using the SPSS version 22.0 software for windows (SPSS Inc, Chicago, IL). Each SNP was evaluated for Hardy-Weinberg equilibrium (HWE) by using an HWE calculator [30 ]. Fisher exact test was used in order to compare genotypes and alleles between groups. Mann–Whitney test was used in order to compute the continuous variables. Odds Ratio (OR) and the 95 % confidence intervals (CI) were calculated by using Unconditional logistic regression in order to examine the association between genotypes and DN. Also, adjusting for potential confounders such as sex, age and diabetes duration was performed by Multivariate logistic regression. Clinical parameters of the subjects were evaluated as Mean ± SD. One-way ANOVA was used in order to calculate the significance of the difference among the clinical parameters of groups. HWE, haplotype construction and frequency analysis were evaluated with SHEsis software [31 (link)]. A p value < 0.05 was considered to be statistically significant.

Statistical analyses were performed using SPSS version 22.0 software for Windows (SPSS Inc.; Chicago, IL, USA). Data are expressed as the mean ± SD, median and number, or frequency and proportion. The significance level was set at 0.05 for each hypothesis. Missing data on the intubation time (originating from intubation failure), POST, and PH (lost to follow-ups) were recorded, but they were excluded from the significance analysis between the two groups. The data distributions were analyzed using the Kolmogorov–Smirnov test or Skewness and Kurtosis tests. For the normally distributed data, the intubation time was analyzed using 2 independent sample t-test. For the non-normally distributed data (delta MAP), Mann–Whitney U test was applied. Levene’s test was used for the equality of variances. We used the Wilcoxon rank-sum test to analyze the ranked data for POST and PH, while χ² test (continuity correction if 1 < frequency < 5; Fisher’s exact test if frequency < 1) was used to evaluate the first-attempt intubation and overall success rates between the two groups.

Statistical analysis was performed using SPSS version 22.0 software for Windows (SPSS Inc., Chicago, IL, USA). The Student t-test and Pearson chi-square test were used to compare preoperative and postoperative characteristics between groups. Kaplan–Meier survival analysis and the log rank test were used to compare the long-term surgical results. Multivariate logistic regression was performed to identify factors affecting undercorrection at the final visit. P-values of less than 0.05 were considered statistically significant. The data are presented as means ± standard deviation unless otherwise specified.

Categorical variables are presented as frequencies and percentages, and continuous variables are presented as medians with 95% confidence intervals (CIs) or means with standard deviations. Variables with a P value no more than 0.20 in the univariate logistic analysis were considered strong risk factors associated with the primary outcome and were put into the multivariate logistic analysis. Variables with P values no more than 0.05 were considered independent risk factors associated with the primary outcome. The RF model was established based on the independent risk factors. The predictive performance of the RF model and the traditional logistic model was validated internally using the concordance c statistic (C-index). The Gini index was applied to describe the importance of the variables in the RF model associated with the primary outcome (Jain et al., 2018 (link)). Statistical analyses were performed using SPSS version 22.0 software for Windows (IBM Corporation, Somers, NY, United States), and the RF model was established in the R package “randomForest” (https://www.stat.berkeley.edu/∼breiman/RandomForests/).