Statistical analysis
Continuous variables and categorical variables were summarized as mean ±
standard deviation (SD) and count (percent) respectively. The Society of
Thoracic Surgery (STS) Risk Score for each patient was calculated using
the online STS calculator [29]. Propensity score matching techniques
were used to control for the difference in the baseline covariates. The
propensity score was estimated using a multivariable logistic regression
model with biological sex as the dependent variable and the baseline
characteristics as covariates including age, BMI, pulmonary disease,
cerebrovascular disease, renal disease, current smoker, past smoker,
hypertension, dyslipidemia, liver disease, gastrointestinal disease,
malignancy, peripheral vascular disease, diabetes mellitus, congestive
heart failure, prior MI, prior percutaneous coronary intervention (PCI),
prior coronary artery bypass grafting (CABG), prior AF/flutter, implant
type, pump time, cross-clamp time, STS score and LVEF. Greedy matching
techniques without replacement and a caliper width equal to 0.2 of the
standard deviation of the logit of the propensity score were applied to
match male patients 1:1 to female patients. Standardized mean difference
was used to evaluate the balance before and after matching. A
standardized difference of 0.1 or less was deemed to be the ideal
balance.
The paired t-test was used to compare the pre- and post-operative
echocardiographic measurements and the absolute changes in LV size, LA
size, and LA volume index between male and female patients. Cox
proportional hazards regression models and the Fine & Gray model
[30] were implemented to determine the hazard ratios of sex
difference on the primary and non-fatal secondary outcomes. McNemar test
was performed to compare the postoperative complications by sex. The
survival curve was plotted for all-cause mortality at longest follow-up
using Kaplan-Meier methods. The interaction between sex and implant type
for all-cause mortality at longest follow-up was tested by adding
implant type and an interaction term of sex and implant type into the
Cox model. Reverse Kaplan-Meier methods were used to estimate the median
follow-up time. Statistical analyses were executed using the SAS 9.4.
(SAS Institute, Cary NC). A p-value <0.05 was deemed of
statistical significance. All statistical tests were two-sided.