2.4 Statistical analysis
RDW cutoff values were used to stratify patients into two groups. Data normality was assessed via the Kolmogorov-Smirnov test, with normally and non-normally distributed continuous values being given as means with standard deviations and medians with interquartile ranges (IQRs), respectively, whereas categorical data were given as numbers (frequencies). These three data types were compared via Student’s t-tests, Mann-Whitney U tests, and chi-squared tests, respectively. Spearman correlation analyses were conducted to assess the association between RDW and LVMI.
The Kaplan-Meier approach was employed to construct survival curves, with data being censored at the more recent follow-up time point if patients failed to reach the pertinent endpoints. Predictors of mortality in HCM patients were identified through univariate and multivariate Cox regression analyses, with the multivariate model being designed to control for potential confounding variables via incorporating them into this analysis. Results were given as hazard ratios (HRs) and 95% confidence intervals (CIs). The prognostic relevance of RDW was then further evaluated by comparing outcomes in patients stratified according to the chosen RDW cutoff value using Kaplan-Meier curves.
Area under the receiver operating characteristic (ROC) curve (AUC) values were used to compare the relative prognostic relevance of RDW alone to that of RDW + LVMI. DeLong’s test was utilized to compare AUC values for these models [23], with calculations being conducted using MedCalc v.19.0.7. P < 0.05 was the significance threshold for this study, and SPSS v25.0 (IBM Corp, NY, USA) was used for statistical testing herein.
Results