Statistical Analysis
Comparisons among the groups were performed using the t -test or Mann-Whitney U test when necessary. Categorical variables are expressed as frequencies and percentages and were compared using the χ2 test and Fisher’s exact test when appropriate. We used the Kolmogorov Smirnov test to evaluate the normality of the continuous variables. Patients with DM were propensity score matched to patients without DM. A logistic model was used to create propensity scores that included the following covariates (including known risk factors for aortic diseases and known factors that could influence the progression and long-term outcomes of aortic diseases): age, gender, body mass index, hypertension, smoking, atherosclerosis, drug abuse, maximum aortic diameter, hematoma thickness, C-reactive protein level and administration of the β-blocker[10][16][17] . A caliper of 0.2 propensity score standard deviations was used to match patients. All further analyses used propensity score-matched patients. Cox proportional hazard models were constructed to evaluate the specific factors associated with aorta-related mortality. Those variables for which the P value < 0.20 in univariate analyses were included in the multivariate analyses. The Fine-Gray model for the competing risk analysis of death was used to estimate the aorta-related and non-aorta-related mortality in different groups during the follow-up period. IBM SPSS Statistics for Windows Version 26.0 (IBM Corp., Armonk, New York) was used for statistical analysis. Differences of P<0.05 were considered statistically significant.