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