Thus, we fitted and compared two different models to strengthen our analysis. As recommended, propensity scores were determined through multivariate logistic regression [12], in which RRT was the dependent variable. Independent variables were related to the probability of receiving the treatment and also outcome for in order to reduce both the bias and the variance in the estimation of treatment effect [13,14]. Independent variables introduced in model 1 were: rising creatinine reflected by maximum RIFLE class, oliguria reflected by the 24-h urine output on reaching maximum RIFLE class, and SAPS II score. Independent variables introduced in model 2 were: blood urea nitrogen, serum creatinine and kaliemia measured on reaching maximum RIFLE class (that is, before RRT was started), fluid accumulation (reflected by the difference between patients’ weight recorded on reaching maximum RIFLE class and that recorded on ICU admission), and SAPS II score.
Using an algorithm [15], we matched patients who received RRT during their ICU stay to other AKI patients who did not on the basis of each of the two propensity scores that we built (model 1 and model 2). Specifically, we sought to match each patient with RRT up to three controls who had the closest propensity score (within 0.05 on a scale of 0 to 1).Besides, patients were also matched on center and period of admission to account for possible inconsistent institutional practices or changes in RRT practices over time. Age (+/- 5 years) was the final matching criterion.
The adequacy of the propensity scores in controlling for treatment selection bias was demonstrated by testing for differences between matched patients in biological parameters likely to trigger RRT on reaching maximum RIFLE class.The goodness of fit and the discrimination of the two logistic regression models used to derive a propensity score for RRT were evaluated by the Hosmer-Lemeshow (HL) test, and the c statistic (area under the receiver operating characteristics curve), respectively.Statistical analysesResults are expressed as numerical values and percentages for categorical variables, and as means and standard deviations (SD) or medians and quartiles [Q1-Q3] for continuous variables.In the whole cohort, comparisons of patients with and those without RRT were based on chi-square tests for categorical data, and on Student’s t-test or Wilcoxon’s test for continuous data, as appropriate.
Comparisons Anacetrapib between matched patients were based on univariate conditional logistic regression. Multivariate conditional logistic regression analysis was used to examine the association between RRT and subsequent hospital mortality, adjusting for variables potentially related to mortality that were not considered in the propensity regression (namely baseline characteristics that had a P value < 0.
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