Abstract
OBJECTIVE: To develop and validate a predictive model for acute kidney injury (AKI) after cardiopulmonary bypass (CPB) surgery in Chinese patients with normal preoperative renal function. METHOD: From January 1, 2015, to September 1, 2022, a total of 1003 patients were included in the analysis as a development cohort. We used the ratio of 7:3 to divide the patients into a training group (n = 703) and a testing group (n = 300). In addition, a total of 178 patients were collected as an external validation cohort from January 1, 2023, to May 1, 2023. In the training group, independent risk factors for postoperative AKI were identified through the least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression analysis. A nomogram predictive model was then established. The area under the curve (AUC) of receiver operating characteristic (ROC) curve, as well as calibration curve and decision curve, were used for validation of the model. RESULTS: Age, body mass index (BMI), emergent surgery, CPB time, intraoperative use of adrenaline, and postoperative procalcitonin (PCT) were identified as important risk factors for AKI after CPB surgery (P < 0.05). The nomogram predictive model demonstrated good discrimination (AUC: 0.772 (95%CI: 0.735 - 0.809), 0.780 (95% CI: 0.724 - 0.835), and 0.798 (95% CI: 0.731 - 0.865)), calibration (Hosmer and Lemeshow goodness of fit test: P-value 0.6941, 0.9539, and 0.2358), and clinical utility (the threshold probability values in the decision curves are respectively > 12%, > 10%, and 16% ~ 75%) in the training, testing, and external validation groups. CONCLUSION: The predictive model, which was established in Chinese patients with normal preoperative renal function, has high accuracy, calibration, and clinical utility. Clinicians can utilize this model to predict and potentially reduce the incidence of AKI after CPB surgery in the Chinese population.