Abstract
This study aimed to develop a predictive model and construct a graded nomogram to estimate the risk of severe acute kidney injury (AKI) in patients without preexisting kidney dysfunction undergoing liver transplantation (LT). Patients undergoing LT between January 2022 and June 2023 were prospectively screened. Severe AKI was defined as Kidney Disease: Improving Global Outcomes stage 3. Using the least absolute shrinkage and selection operator (LASSO) analytics, we identified the preoperative, intraoperative, and postoperative factors associated with severe AKI. Machine learning were employed to develop predictive models, and the most suitable model was selected for further analysis. The Shapley Additive Explanation was utilized to construct graded nomograms, forming the Severe AKI post-LT (SALT) scale. Among the 405 patients, 44 had AKI stage 3 (severe AKI). The Model for End-Stage Liver Disease (MELD) score, estimated blood loss, alanine aminotransferase, D-dimer, and thromboelastography reaction time within 24 h post-LT were identified as risk factors. The logistic regression model achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.885. The graded SALT scale, based on the logistic regression model, achieved AUROCs of 0.751, 0.826, and 0.894. The AUROCs for the testing cohort is 0.791. This preliminary study provides a SALT scale for assessing the occurrence of severe AKI after LT. Although additional data are needed to externally validate our model before applying it to patient care, our findings suggest that the SALT scale may be a feasible bedside tool for assessing the risk of AKI after LT.