Spss statistics program version 24

Statistical analyses were performed using the SPSS statistics program version 24 (IBM Corporation, New York, NY) and MedCalc version 18 (MedCalc Software, Mariakerke, Belgium). Quantitative variables are presented as the median and interquartile range (IQR: 25th–75th percentiles). The Mann–Whitney test was applied to compare nonparametric quantitative variables between the two groups, whereas the Kruskal–Wallis test was used to compare more than two groups. The chi-square test was used to compare the distribution of the two groups.
Receiver operating characteristic (ROC) curves were used to compare the IG% and other parameters between the sepsis and non-sepsis patient groups. The IG% diagnostic cut-off value with the best combined sensitivity and specificity was also determined. The area under the curve (AUC) and 95% confidence interval (CI) were calculated for each plot.
A logistic regression analysis was performed to evaluate the predictive value of the IG% for sepsis. Univariate and multivariate regression analyses were performed for the biomarkers and risk factors. Statistical significance was set at P < 0.05.

Statistical analyses were performed using the SPSS statistics program version 24 (IBM Corporation: New York, NY, USA) and R statistical software (version 3.6.3; R Foundation for Statistical Computing: Vienna, Austria). The Mann–Whitney test was applied to compare nonparametric quantitative variables between the two groups, whereas the Kruskal–Wallis test was used to compare more than two groups. Receiver operating characteristic (ROC) curves were used determine the classifying ability of MRSA and area under the curve (AUC) and 95% confidence interval (CI) were calculated. Statistical significance was set at p < 0.05. Predictive performance of the machine learning model for MRSA prediction was presented as sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), accuracy, and Cohen’s Kappa Coefficient as follows: Sensitivity = (True Positives (MRSA))/(True Positives (MRSA) + False Negatives); Specificity = (True Negatives (MSSA))/(True Negatives (MSSA) + False Positives); LR+ = Sensitivity/(1-Specificity); LR− = (1-Sensitivity)/Specificity; Accuracy = (True Positives (MRSA) + True negative (MSSA))/(True Positives (MRSA) + True Negatives (MSSA) + False Positives + False Negatives). The sensitivity and specificity indicated the proportion of correct predictions for positive (MRSA) and negative (MSSA) samples, respectively [28 ].

An analysis of variance (ANOVA) followed by a Duncan test (p < 0.05) was performed to determine if significant differences existed in the Enterobacteriaceae mean log CFU/g values of all samples. In that case, no comparaison between groups was carried out—neither anatomical parts nor retailers. The same procedure was carried out with the kinetics parameters. On the other hand, Student’s t-test was carried out for comparing Enterobacteriaceae mean log CFU/g values between retailers (supermarkets vs. butchers) and types of samples (hindquarters vs. livers). It was established that significant differences existed when p < 0.05.
All the statistical analyses were carried out using the IBM SPSS Statistics program version 24.