Molecular Biomarkers and Risk Prediction Models for Acute Kidney Injury Following Liver Transplantation: A Review.

Liver transplantation is the definitive treatment for end-stage liver disease; however, postoperative acute kidney injury (AKI) affects 30%-70% of recipients and significantly worsens prognosis. The pathogenesis involves insufficient preoperative hepatorenal reserve, intraoperative hemodynamic instability, and immunosuppressant nephrotoxicity. Despite the evolution of diagnostic criteria from RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease) and AKIN (Acute Kidney Injury Network) to KDIGO (Kidney Disease: Improving Global Outcomes), creatinine-based markers remain insensitive and delayed, hindering early detection. Diverse predictive models have been developed to improve risk stratification, including conventional scoring systems (Model for End-Stage Liver Disease, Acute Physiology and Chronic Health Evaluation-II, and Sequential Organ Failure Assessment), multivariate logistic regression, and machine learning algorithms such as artificial neural networks, random forest, and XGBoost, which can achieve area under the curve values of 0.75-0.91. However, clinical implementation remains limited by inconsistent variable definitions and insufficient external validation. Concurrently, novel molecular biomarkers have shown promise for early prediction. Tubular injury markers (NGAL and KIM-1) and cell cycle arrest biomarkers ([TIMP-2]×[IGFBP7]) can detect renal injury within 0-6 h, whereas cystatin C provides reliable assessment at 6-48 h post transplantation. Additional candidates include inflammatory mediators (IL-18 and soluble ST2), endothelial dysfunction markers (soluble Syndecan-1 and endothelin-1), and emerging molecules such as proenkephalin, C-C motif chemokine ligand 14, C-X-C motif chemokine receptor 10, and miR-21-5p. Integrating biomarkers across temporal windows with mechanistic pathways enhances predictive accuracy and may facilitate stage-specific interventions. This review synthesizes recent advances in predictive models and molecular biomarkers for AKI following liver transplantation, evaluating their performance, limitations, and translational potential while identifying future directions for improving patient outcomes.