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.