Development of a risk prediction model for acute kidney injury in liver transplant recipients - Wang, Dahui; Chen, Xin; Tang, Yanchen; Xiao, Wei; Xu, Kangqing; Deng, Jiehua (Frontiers in Surgery, 2025)
ObjectiveTo develop and internally validate a nomogram for early postoperative prediction of acute kidney injury (AKI) within 7 days after orthotopic liver transplantation (LT).MethodsWe retrospectively analyzed 500 orthotopic liver transplants at the First Affiliated Hospital of Sun Yat-sen University (January 1, 2016–April 30, 2022). Patients were randomly split into training (n = 352) and validation (n = 148) cohorts for same-center internal validation using a random-split design. AKI within 7 postoperative days was defined by KDIGO serum-creatinine criteria only (KDIGO-SCr) because urine-output data were incomplete. Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) and entered into multivariable logistic regression to build a parsimonious nomogram for early postoperative (first 6–12 h) risk stratification and monitoring. Performance was assessed by AUC and calibration; decision-curve analysis illustrated relative net benefit without prespecified thresholds or actions.ResultsBMI, operation time, intraoperative urine volume, and postoperative levels of urea nitrogen, blood ammonia, and procalcitonin were identified as independent risk factors for AKI after LT (P < 0.05). The nomogram demonstrated good discrimination, calibration, and clinical usefulness in both the training and validation cohorts, with an AUC of 0.769 (95% CI: 0.715–0.823) in the training cohort and 0.704 (95% CI: 0.618–0.790) in the validation cohort.ConclusionThe nomogram predictive model developed in this study shows good accuracy and can be conveniently applied for early identification and risk prediction of acute kidney injury following liver transplantation.