Categorical variables selleck chemicals llc are presented as frequency and percentage. Baseline characteristics of septic and nonseptic patients were compared using the Mann-Whitney U test for quantitative variables. A Kruskal-Wallis test with Dunn’s multiple comparisons post-test was used for subset analysis and comparisons with healthy subjects, and a Chi-square test (or the Fisher’s exact test when frequency was less than five) was selected for proportion comparisons. Correlations between molecular parameters were analyzed using the Spearman r test. Models were built up sequentially starting with the variable most strongly associated with sepsis diagnosis and continuing until no other variable reached significance. When the final model was reached, each variable was dropped in turn to assess its effect.

Different models were compared using the likehood ratio test, keeping in the final one variables significant at the P = 0.05 level.Table 2General characteristics of studied groups*Different univariate logistic regression models were performed to evaluate which biological or clinical parameters can predict early sepsis diagnosis. Variables included in the analyses were: (i) cortisol baseline; ACTH, apelin, SDF-1��, AVP, copeptin, PCT (for molecular parameters), as well as (ii) age; APACHE II score, sepsis score, gender, shock on admission, (continuous variables for the three formers and binary for the two last parameters). Because normal distribution of biological values was not reached, the selected parameters were categorized: cutoffs values were determined by optimal likelihood ratios of individual receiver operating curve (ROC) analysis, or according to the manufacturer’s recommendation for PCT.

Different multivariate logistic regression models with a stepwise selection procedure were then performed with categorical variables reaching significance in the univariate analysis. Different models were tested to compare the impact of PCT or sepsis score inclusions or not and areas under the ROC curves (AUC) were calculated both for the models and for each of the predictive variables, to compare if one model has one a better sensitivity/specificity than PCT or sepsis score alone. Optimal ROC curves were established with categorical variables, using a probability score to predict early sepsis diagnosis derived from a multivariate regression equation, as described by Shapiro et al [20].

The relationship between two parametric or non parametric variables was assessed using both InStat version Anacetrapib 3.0 for basic between-group comparisons, SPSS version 16.0 (Chicago, IL, USA) for logistic regression analyses and MedCalc version 10 (Mariakerke, Belgium) for ROC calculations. P �� 0.05 was considered statistically significant for all the performed tests.ResultsGeneral patient dataGeneral characteristics of the studied population and groups are detailed in Table Table2.2.