SPSS version 25.0 and GraphPad Prism 9.4.1 (681) were used for statistical analysis and plotting. Normally distributed continuous variables are presented as mean and standard deviation. Non-normally distributed continuous variables are presented as medians and interquartile ranges. Categorical variables are expressed as frequencies and percentages.
The independent sample t-test, the Mann–Whitney U test, and the chi-square test were used to compare the differences of continuous variables and categorical variables between the two groups. All variables with a significance level of P < 0.10 on the univariate test were included in further multivariate analyses. Three linear regression models were then developed. Model 1 was the basic model, including LVH and accepted demographic data (age, sex, BMI, and education level). Model 2 was adjusted for cardiovascular risk factors (diabetes mellitus, CVD, common CAS, smoking history, beta-blockers, pulse pressure, and EF) based on Model 1. Model 3 was adjusted for laboratory parameters (hemoglobin, albumin, hsCRP, and Ccr) based on Model 2. Next, the risk factors of CI were analyzed by multivariate logistic regression analysis.
To reduce the effect of possible selection bias, the effect of the small number of patients with LVH, and the effect of the relatively large number of associations on the reliability of the multivariable model and to adjust for the effects of other potential confounders without reducing the ratio of events per variable, we further performed sensitivity analyses for LVH by propensity score matching. Propensity score matching was performed using the following variables: age, sex, BMI, RRF, CVD, pulse pressure, and EF. The maximum difference in propensity scores that allowed matching was 0.02, and all test levels were two-sided, with P < 0.05 indicating statistical significance.
The independent sample t-test, the Mann–Whitney U test, and the chi-square test were used to compare the differences of continuous variables and categorical variables between the two groups. All variables with a significance level of P < 0.10 on the univariate test were included in further multivariate analyses. Three linear regression models were then developed. Model 1 was the basic model, including LVH and accepted demographic data (age, sex, BMI, and education level). Model 2 was adjusted for cardiovascular risk factors (diabetes mellitus, CVD, common CAS, smoking history, beta-blockers, pulse pressure, and EF) based on Model 1. Model 3 was adjusted for laboratory parameters (hemoglobin, albumin, hsCRP, and Ccr) based on Model 2. Next, the risk factors of CI were analyzed by multivariate logistic regression analysis.
To reduce the effect of possible selection bias, the effect of the small number of patients with LVH, and the effect of the relatively large number of associations on the reliability of the multivariable model and to adjust for the effects of other potential confounders without reducing the ratio of events per variable, we further performed sensitivity analyses for LVH by propensity score matching. Propensity score matching was performed using the following variables: age, sex, BMI, RRF, CVD, pulse pressure, and EF. The maximum difference in propensity scores that allowed matching was 0.02, and all test levels were two-sided, with P < 0.05 indicating statistical significance.
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