Software

We compared high and low nutritional risk using the NUTRIC and modified NUTRIC scores. Categorical variables were compared using the chi-square test and continuous variables using Student’s t-test or the Wilcoxon–Mann–Whitney test. The model’s discrimination for predicting 28-day mortality was assessed by the area under the receiver operating characteristic (ROC) curve for both the NUTRIC Score and modified NUTRIC Score. The ROC curves of the two scores were compared using MedCalc software (version 1.76; MedCalc software, Ostend, Belgium). All other statistical analysis was conducted using SPSS software (version 21.0; SPSS Inc., Chicago, IL, USA). All significance tests were two-sided; a p-value < 0.05 was considered significant.

The aim of this work was to analyze the health status of the child population of the Chernivtsi region, especially the dynamics of the prevalence and incidence of cystitis. The official statistical data have been studied (reports on the state of medical care for children in the Chernivtsi region and data from the Center of Medical Statistics of the Ministry of Healthcare from 2006 to 2017). This work is a fragment of the scientific and research paper: “Scientific support, monitoring and evaluation of models of health care development in Ukraine at the regional level” (due date 2015–2017), state registration no. 0115U002852. To substantiate the provisions and identification of priority approaches to optimize and improve the quality of medical care at the regional level, the peculiarities of the dynamics of the prevalence and incidence of cystitis among the child population of the Chernivtsi region have been analyzed.
Statistical data were calculated and compared using the MedCalc software, developed by “MedCalc software” (Ostend, Belgium). The immunohistochemical data are reported as mean±SEM. The data obtained were statistically processed using the Mann-Whitney U test. Bivariate correlation between variables was determined by Pearson’s correlation coefficients. A p-value <0.05 was considered significant. Results are represented as mean±SD.

GraphPad Prism Software (version 8.3.0, CA, United States), Medcalc software (Ostend, Belgium), and Analyse-it Software (version 5.66, Leeds, United Kingdom) were used to statistically analyze all data. Linear regression analysis was performed by GraphPad to estimate the association between the assays. The Bland–Altman difference plot, which can be drawn by Medcalc software, is helpful in demonstrating the potential relationship between the differences and the magnitude of measurements exhibiting any systematic bias and in identifying possible outliers. Weighted Deming regression was performed by Analyse-it to complete the data comparison.

Statistical analysis was performed with R software (version 4.1.0, http://www.R-project.org), SPSS software (version 19.0, IBM, Armonk, NY, USA), and MedCalc software (version 19.8, Mariakerke, Belgium). The Chi-Squared test or Fisher exact test and the Student t-test or Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. The ICC and LASSO regression was performed using the “irr” and “glmnet” packages. The nomogram and DCA were plotted by the “rms” and “rmda” packages. The MedCalc software was used to perform the ROC curves. Delong test was performed to compare different AUCs. A value of p < 0.05 was considered as statistically significant.

For each factor, univariate and multivariate analyses were utilized. When p< 0.30 was obtained by univariate analysis, the variables were subjected to multivariate analysis. Pearson’s χ² test and the Mann–Whitney U test were used individually to compare binary and continuous data. The Hosmer–Lemeshow chi-square statistic was applied to assess the goodness of fit for comparing observed and projected outcomes at different risk deciles. Using MedCalc software, the predictive power of the perioperative variables identified in multivariate analyses was assessed by calculating the area under the curve (AUC) value of receiver operating characteristic (ROC) plots. AUC values ranging from 0.7 to 0.9 indicate good predictive value, while values less than 0.7 indicate poor predictive value. p< 0.05 was considered statistically significant. Data analysis was performed using SPSS software (version 25.0) and MedCalc software (version 11.4.2.0).