Outcomes and statistical analysis
We analyzed pre-, intra-, and early postoperative outcomes. Operative
mortality is expressed as procedural mortality, hospital death and
30-day mortality. Neurological complications were divided into permanent
and temporary, depending on their presence at discharge, and included
focal (stroke), non-focal (coma) and spinal neurologic deficit
(paraplegia and paraparesis). The exact definitions of these
complications have been provided previously [10]. Myocardial
infarction was defined as ischemia diagnosed based on clinical symptoms,
new typical ECG changes, decreased local regional wall contractility and
elevated biomarkers, and requiring pharmacological, percutaneous or open
surgical intervention. Low cardiac output syndrome was defined as
patients requiring significant pharmacological support, extracorporeal
membrane oxygenation, or ventricular assist device. Pulmonary
complications were classified as acute respiratory distress syndrome,
pneumonia, pulmonary edema and severe atelectasis, as well as prolonged
mechanical ventilation. Reexploration for bleeding included only cases
with acute postoperative bleeding (diffuse or with a defined source of
bleeding), requiring urgent or emergent intervention. Elective open
revisions for pericardial effusion were not included in this definition.
Gastrointestinal complications including prolonged ileus
(>72 h) or gastric paresis, or new hepatobiliary
disfunction (with metabolic acidosis or increase in lactate) were
recorded [11].
Data were analyzed using GraphPad Prism Version 7.00 (GraphPad Software,
La Jolla, Ca, USA). The distribution of continuous variables was
evaluated using the Kolmogorov-Smirnov test and Q-Q plots. Continuous
variables are expressed as mean ± standard deviation (when normally
distributed) or median and range (non-normal distribution). Categorical
data are reported as frequencies and percentages. Multivariable logistic
regression was used to determine the independent predictors of
in-hospital mortality. Clinically relevant preoperative risk factors for
hospital mortality were selected using univariate analysis where p value
of less than 0.05 was considered statistically significant. The variance
inflation factor (VIF) was evaluated in order to exclude
multicollinearity (VIF of 4.0 or more indicated intercorrelation between
the analyzed variables). Multivariate logistic regression model was
controlled by means of Hosmer-Lemeshow goodness-of-fit test and the area
under the receiver operating characteristic curve (ROC).