Spss 22.0 statistical package program for windows

All statistical analyses were performed using the SPSS 22.0 Statistical Package Program for Windows (SPSS, Inc., IL). The continuous variables were presented as the mean values ± standard deviation and median with interquartile ranges and the categorical variables were expressed as frequency and percentage. Kolmogorov–Smirnov test was used for the evaluation of normality of distribution. The differences between the 2 groups were assessed by using Student t-test for normally distributed variables and Mann–Whitney U test for variables without normal distribution. The Chi-square test or Fisher exact test were used to compare categorical variables as appropriate. We used a univariate logistic proportional regression analysis to evaluate the association of each variable on the occurrence of apical thrombus. The receiver operating characteristic curve analysis was used to determine the best predictive SII level for apical thrombus formation. Youden index was used to establish the most appropriate cutoff value. Patients were divided into 2 quantiles according to the occurrence of apical thrombus and the optimal cutoff value of SII. Correlations were examined using Pearson correlation test. A P-value < .05 (using a 2-sided test) was considered significant.

Continuous variables with a normal distribution (eg, age, LVEF, peak cardiac troponin I [cTnI] level, and creatinine level) were expressed as a mean (SD), and the differences between groups were tested by independent samples t test. Continuous variables with nonnormal distribution (eg, hs-CRP and hematological parameters) were expressed as medians and interquartile ranges, and the differences between the groups were analyzed with Mann-Whitney U tests. Categorical variables were summarized as percentages and compared to the χ² test. A 2-sided P value <.05 was considered significant. Multivariate logistic regression analysis was used to identify independent predictors for the development of the no-reflow phenomenon. Receiver–operating characteristic (ROC) curves of the NLR, PLR, MPVLR, lymphocyte-to-monocyte ratio (LMR), lymphocyte count, and neutrophil count were drawn to compute the area under the curve (AUC). The Youden-index method was applied to set an optimal cutoff value for optimal differentiation. The DeLong test was used to compare ROC-AUCs of different parameters. Statistical analysis was performed using the SPSS 22.0 Statistical Package Program for Windows (SPSS Inc, Chicago, Illinois).