Statistical analysis
Data distributions were checked for normality before further analysis
with the Shapiro–Wilk test. Continuous data are presented as the mean
and standard deviation (SD) or median and interquartile range (IQR).
Unpaired t-tests or Wilcoxon rank sum tests were used for statistical
comparisons. Categorical data are presented as proportions and were
compared using the chi-squared tests.
A generalized linear model (logit model) and linear model was used to
assess the effect of multiple variables on in hospital mortality and
post-operative LOS. Candidate explanatory variables (n=10) included
were: MetS/no-MetS, age, sex, left ventricle ejection fraction (LVEF),
renal failure, chronic obstructive pulmonary disease (COPD), redo
surgery, atrial fibrillation (AF), peripheral vascular disease and
previously treated coronary artery disease. In order to examine the
effect of MetS stratified for each of the cohort of treatments (MVS,
SAVR and TAVR), interaction term was added to the model
(MetS*Treatment), keeping mitral intervention as reference level.
Linearity assumptions were checked
(cran.r-project.org/web/packages=sjPlot).
As sensitivity analysis, regression model was also built including the
risk factors incorporated in the MetS definition such as:
BMI>30 kg/m2 and as continuous variable,
systemic hypertension, atherogenic dyslipidaemia and insulin resistance.
The models were also tested for multicollinearity with variance
inflation factor (VIF), and explanatory variables with VIF higher than 5
were excluded since considered poor regression estimates
(cran.r-project.org/web/packages=VIF). Results were presented as
adjusted Odds Ratio (adj. OR) and beta-coefficients and 95% confidence
intervals (Cis) (models diagnostic in Supplementary Material ).
30-, 60- , 90- and 120-days in hospital-survival probability was also
analysed using the Kaplan-Meier and corresponding survival curves were
built by plotting all observations. Comparisons of survival estimates
for two different patient strata (overall MetS vs. no-MetS) were
performed with the log-rank statistic
In between centres mortality variability was also evaluated and pooled
mortality proportion (overall MetS vs. no-MetS) plotted along with the
prediction interval using a random effects model
(cran.r-project.org/web/packages=meta).
All statistical analyses were performed with RStudio Team (2020)
(RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA.