Statistical analysis
BROWN
The distribution of continuous variables was assessed with the Kolmogorov–Smirnov test. Data are expressed as mean ± standard deviation (SD) or median (interquartile range), as appropriate. Categorical variables are reported as fractions and percentages (%). Differences between parametric variables were evaluated with the Student’s t test for unpaired and paired measures or with the Mann–Whitney and Wilcoxon tests, as appropriate. Fisher’s exact test was adopted for categorical variables. LSD and Scheffé’s test were used to correct for multiple comparisons.We used two-way ANOVA to detect significant variable changes across time points between two groups. We calculated a receiving-operator characteristic (ROC) curve using the Wilson–Brown method to summarize the ability of each measure to predict fluid responsiveness across the various thresholds. For all predictors, we identified the optimal threshold based on the Youden index J (Sample size calculation for the primary outcome was made considering a correlation between CDPV changes and CI changes during EEOt of 0.8 with a null hypothesis of correlation set at 0.5. With beta error at 0.8 and a two-sided alfa error of 0.05, 38 measurements were needed to test the study hypothesis. For the area under the ROC curve (AUROC), we considered an AUROC of 0.75, a null hypothesis of 0.5, and beta and alpha error as above. The total required sample was 40 measurements.For the carotid Doppler variables, we calculated how many measurements fell within the gray zone, i.e., the number of cases with a sensitivity < 90% and/or a specificity < 90%.We also estimated the precision and the least significant change (LSC) [Precision was estimated asThe LSC was calculated asWe used SPSS® 28 software (IBM Chicago IL), MedCalc® Statistical Software version 20.210 (MedCalc Software Ltd, Ostend, Belgium; 