Machine learning-based nomogram for acute kidney injury after liver transplantation

Acute kidney injury (AKI) after liver transplantation (LT) is a frequent complication associated with adverse outcomes, yet practical early postoperative prediction tools remain limited. This retrospective study developed and internally validated an interpretable model for AKI after LT using perioperative variables available by the time of the first urgent postoperative blood test, generally obtained within 1 h after surgery. Adult LT recipients treated from June 2023 to January 2026 were screened. AKI was defined according to Kidney Disease: Improving Global Outcomes criteria. Among 146 screened recipients, 127 were included and randomly divided into training and validation cohorts at a 3:1 ratio. Least absolute shrinkage and selection operator regression selected estimated glomerular filtration rate calculated using the CKD-EPI equation, anhepatic phase time, postoperative natural logarithm-transformed D-dimer, and postoperative alanine aminotransferase. Multivariable logistic regression showed that lower estimated glomerular filtration rate and longer anhepatic phase time were independently associated with AKI. The nomogram achieved area under the receiver operating characteristic curve values of 0.861 in the training cohort and 0.761 in the validation cohort. Five-fold cross-validation and bootstrap internal validation were performed to assess model stability and optimism. Among five evaluated models, support vector machine showed the numerically highest validation AUC of 0.846. Shapley additive explanations identified estimated glomerular filtration rate as the dominant predictor. This early postoperative model may help identify high-risk recipients for intensified renal monitoring and individualized management. External multicenter validation is required before routine clinical implementation.