Spss software package version 21

Categorical variables were expressed as frequency and percentage, and continuous variables were expressed as mean ± standard deviation (95% confidence interval). P value of less than 0.05 was considered significant. IBM SPSS software package version 21.0 (SPSS, Chicago, IL, USA) was used for statistical analysis. The receiver-operating characteristic (ROC) curve was drawn to calculate the area under the curve (AUC). Values above 0.7 for AUC-ROC were considered to have good test performance. Sensitivity, specificity, and positive and negative predictive values of serum NGAL measurements for detecting AKI were calculated.

The descriptive demographic data were represented as frequency, percentage, and mean ± standard deviation. Chi-square (χ²) was also carried out to examine the relationship between categorical qualitative variables (e.g., disease type (complete or incomplete) and complication type). The Kolmogorov–Smirnov test was used to assess the sample distribution normality. The Mann-Whitney U test was then used based on the non-normality of the quantitative data. The correlation between quantitative variables was assessed by the Spearman correlation coefficient given the absence of normal distribution of values. Moreover, possible clinical symptoms and laboratory findings to estimate the incidence of complications and the response to treatment were investigated using binomial and multinomial logistic models. The SPSS software package version 21.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data at a significant level of p < 0.05.

The data at a significant level of p < 0.05 were analyzed using the SPSS software package version 21.0 (SPSS Inc., Chicago, IL, USA). The descriptive data are represented as frequencies, percentages, and means ± standard deviations. The significance of differences between means and frequencies was assessed using independent t-test (continuous variables) and Chi-square (χ², categorical variables). Multiple means were compared using analysis of variance (ANOVA). A multiple logistic regression analysis was also carried out to assess the independence of association of demographic, clinical and laboratory factors with DMSA scan changes. Pearson’s coefficient was considered to find any significant correlation between tested variables.

For processing and statistical analysis of all data, the SPSS software package, version 21.0 (SPSS GmbH, Munich, Germany), was used.
Descriptive statistics were computed for continuous and categorical variables. The statistics computed included mean and standard deviations (SD) of continuous variables, frequencies and relative frequencies of categorical factors and are presented as mean ± SD.
Because of the small number of subjects and the low power of a test on normality comparisons between groups were done by using nonparametric Kruskal-Wallis test or Mann-Whitney U test by ranks and correlations were computed by using Pearson’s correlation coefficient.
All p-values resulted from two-sided statistical tests and values of p < 0.05 were considered to be statistically significant.

Statistical analysis was performed using the SPSS software package, version 21.0 (SPSS, Inc., Chicago, IL, USA). As the sensitivity of the assay was 0.15 mg/L, observations of hs-CRP within the interval of concentrations from 0 to 0.15 mg/L (33% of the children) were set to 0.15 mg/L. Children with hs-CRP levels equal or above 10 mg/L were excluded of the analysis. As a sex-dependent association between diet and hs-CRP has been previously described [15 (link)], sex-stratified analyses were performed. Any dietary variables that were not normally distributed were log transformed to normality prior to analyses. We used a t-test or Mann-Whitney test to compare nutrient intake by sex. ANOVA or Kruskal-Wallis test and the corresponding Post Hoc test were used to compare the means of nutrient and food intake depending on hs-CRP levels tertile. To identify major dietary patterns experimental factor analysis was conducted. The identified factors were rotated using a Varimax rotation to ensure that standardized, non-correlated dietary patterns were identified. The Kaiser–Meyer–Olkin (KMO) test was used to evaluate data adequacy for factor analysis. Eigen values higher than one and the Screen plot determined whether the factor should be retained. Regression factor scores were used for partial correlation analyses investigating the association between dietary patterns and hs-CRP.

Continuous variables were expressed as the means ± SD. Categorical variables were expressed as percentages. Comparisons between groups were performed using Pearson's chi-square test for categorical variables and the Mann-Whitney U test or Student's t-test for continuous variables, where appropriate. To achieve a normal distribution, the BNP value was log-transformed before the analysis. To assess the dependent determinants of the log BNP, a multiple regression analysis was performed after the simple regression analysis. Age, gender (0 for females and 1 for males), IHD (0 for non-IHD and 1 for IHD), atrial fibrillation (0 for non-AF and 1 for AF), the LVEF, age, BMI, and s-Cr were included as variables in the multiple regression analysis. In addition, a gender-segregated multiple logistic regression analysis was performed to determine predictive factors for IHD using age, LVEF, BMI, s-Cr, atrial fibrillation (0 for non-AF and 1 for AF), and logBNP. A value of p<0.05 was considered to be statistically significant for all data that were statistically analyzed using the SPSS software package, version 21.0 (SPSS Inc., Chicago, IL).