Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

BACKGROUND: Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI. METHODS: A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model. RESULTS: In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121–2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316–7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001–1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893–17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737–0.894). CONCLUSION: The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.