Statistical analysis Statistical method of Y-27632 cost the factor analysis was used to extract the risk aspects for the patients (Statgraphics Centurion XVI, StatPoint Technologies, Inc. Warrenton, USA). Then, the clinical value

of the extracted factors was evaluated by ANOVA, where the treatment outcome was investigated. Variances were checked by Levene’s test. As p value for this statistics was less than 0.05, Kruskal-Wallis Test was applied to check the significance. Finally, the number of significant preoperative factors for the prognosis was reduced to 8 parameters which were grouped into 3 prognostic factors named respectively: proteinic status, inflammatory status and general status arranged dependently on their statistical power. All utilized parameters can be collected in a simple way during examination of the patient directly after admission to the ward and after laboratory investigations (within 2–3 hours). The first factor explained as “proteinic

status” informs about the initial state of protein metabolism. This parameter is composed of results of laboratory tests of blood: serum protein, albumin and hemoglobin (HGB) level. The second factor “inflammatory status” allows to estimate the patient’s septic state on the basis of three laboratory parameters determined prior to the treatment: white blood cell count (WBC_pre), CRP value (CRP_pre), PCT value (PCT_pre). The third factor of the prediction schema “general risk” focuses on the evaluation of the patient’s clinical state and includes selleck compound only two important parameters: age (Age) and the number of coexisting diseases (Coex_disease). Coefficients of sensitivity (SNC) and specificity (SPC) were calculated for the extracted

factors to check the prediction power of the suggested method. The proposed method is designed for the prediction of recovery. Thus, the result of the test is positive (P) if the test predicts the recovery, and negative Amino acid (N) if the test does not predict the recovery but i.e. “death”. Respectively, the result of the test is true (T) if the test predicts recovery when the observed result is “recovery”, and the result of the test is false (F) if the test does not predict the recovery. Therefore: TP-patient recovered and predicted as “recovery”, TN-patient died and predicted as “death”, FP-patient died but predicted as “recovery”, and FN – patient recovered but predicted as “death”. Basing on the above definitions, the suggested sensitivity and specificity coefficients equations are: Sensitivity coefficient: Specificity coefficient: Results Three factors have been extracted as statistically requested (Eigenvalue > 1), they are presented in Table 3. Together they account for over 69% of the variability in the original data.