Statistical Analysis Sample size analysis predicted that a total of 131 MV patients in each group would be needed to detect a relative difference between treated and non-treated patients of at least 60% (decrease from 20% to 6%) for mortality rate based on two-sided test with type I error of 0.05 or less and 80% power of 80%. Quantitative variables will be expressed as mean and standard deviation. Categorical variables will be represented by percentages. Univariate analyses exploring associations between individual risk factors and primary/secondary outcomes were performed using Fisher's exact test for dichotomous variables and Student's t test for continuous variables. A multivariate logistic regression model is performed to investigate the effect of relevant risk factors and relative contributions of the various risk factors. Goodness of fit is evaluated through the log-likelihood of the fitted model. Odds ratios and their 95% confidence interval are calculated in the logistic regression model to describe the strength of the relationship between the categorical risk factors and outcomes. All tests are two-sided, and a p-value of 0.05 is considered to be statistically significant. All analysis is carried out using SAS software version 6.12 (SAS Institute Inc, Cary, NC).