Statistical analysis
Categorical variables are described as numbers and percentages and quantitative variables as means ± standard deviations. Groups were compared using the chi square test, Fisher’s exact test, Student’s t-test or the Wilcoxon test as appropriate. To determine independent predictive risk factors for failure of mid-cavity VAD with the dependent variable being binary, we first performed a bivariate analysis and then a multivariate analysis using logistic regression23. Variables with a level of significance less than or equal to 0.20 in the bivariate analysis were included in the multivariate model, which was analysed with a stepwise logistic regression. Interaction effects were sought for all variables included in the model. Model discrimination was assessed by the C-index, which is identical to the area under the receiver operating characteristic (ROC) curve. Calibration was assessed by the Hosmer-Lemeshow goodness-of-fit statistic23. Internal validation was performed by resampling our study population (5000 bootstrap samples). To predict the failure of mid-cavity VAD, a ROC curve was used to determine a threshold of the probability issued from the logistic multivariate regression.
All statistical analyses were performed with a significance level set at 0.05, with SAS software, version 8.02 (SAS Institute, Inc, Cary, NC).