Statistical analysis
Patients were randomly assigned to the development and validation groups at a ratio of 7:3. The clinical expertise and boxplot plots were used to check for extreme values in the continuous variables. Then, continuous variables (preoperative LVEF, baseline eGFR, cardiopulmonary bypass time, postoperative hemoglobin level, postoperative white blood cell count, postoperative serum magnesium level, postoperative carbon dioxide combining power, and postoperative serum uric acid level) were winsorized at 1% and 99% to reduce the effect of the extreme values. Multiple imputation by chained equations with 20 iterations was used to estimate the missing data, and the results were pooled according to the Rubin's rule. All variables, including the outcome variable, were included in the imputation model, which was recommended for handling missing data.Restricted cubic splines were used to analyze the potential nonlinear association between continuous variables and the outcome risk.The final model was validated in the development and validation groups, respectively. The performance of the model was assessed by its discrimination, calibration, and clinical utility. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination. In general, the AUC > 0.7 indicated a good discrimination. The calibration of the model was assessed by the calibration curve and examined utilizing the Spiegelhalter All analyses results for developing and validating the model were in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. R software (version 4.2.1; 