Statistical analysis

Statistical analysis was performed using JMP statistical software (version 9.0; SAS Institute, Cary, NC, USA). Continuous variables were reported as mean ± SD or median with interquartile range (IQR), as appropriate, and were compared using Student’s t-test. Categorical variables were reported as counts with percentages and were compared using the chi-squared test. Univariate and then multivariate logistic regression analyses were performed to identify pre- or intraoperative factors associated with postoperative AKI. Variables significantly associated with postoperative AKI (P < 0.05 in univariate analysis) were included in the multivariate models; the model with the lowest Akaike information criterion (AIC) was subsequently selected as the final model. The difference in postoperative AKI occurrence between the cohorts 2002–03 and 2011–13 was determined using logistic regression. We adjusted the odds ratio (OR) for differences in baseline clinical characteristics between the two cohorts, including history of hypertension, coronary artery disease, myocardial infarction, left ventricular ejection fraction (LVEF), Charlson comorbidity score and preoperative eGFR. Postsurgery patient survival in those with and without AKI was presented using a Kaplan–Meier plot and was compared using the log-rank test. Cox proportional hazards analysis was used to examine the association between AKI and all-cause mortality. A two-tailed P-value  <0.05 was considered statistically significant.