Predicting Post-Liver Transplantation Mortality: A Retrospective Cohort Study on Risk Factor Identification and Prognostic Nomogram Construction

Background To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimize clinical decision-making in liver transplantation (LT) recipients. Methods This retrospective cohort study analysed 173 allogeneic LT recipients at the Affiliated Hospital of Zunyi Medical University between August 2019-December 2023. Clinical and biochemical variables were systematically collected, including recipient profiles (age, gender, prior abdominal surgery Performance Status (PS) scores), biochemical markers (serum creatinine, sodium, albumin, total bilirubin, neutrophil/lymphocyte counts), and prognostic scores (Model for End-Stage Liver Disease (MELD), MELD-sodium (MELD-Na), Child-Turcotte-Pugh (CTP), neutrophil-to-lymphocyte ratio (NLR), albumin-bilirubin (ALBI)). Intraoperative metrics such as blood loss volume and anhepatic phase duration, were also recorded. Univariate and multivariate Cox regression identified mortality predictors. LASSO-regularized Cox regression facilitated variable selection and nomogram construction. Internal validation used decision curve analysis (quantifying clinical net benefit) and time-dependent receiver operating characteristic (ROC) curve analysis (12/18/24-month area under the curve (AUC)). Kaplan-Meier survival analysis stratified patients into tertiles. Results Univariate analysis identified MELD score > 25, blood loss > 5 L, PS score, neutrophil count, total bilirubin level, and MELD-Na score as significant predictors ( p  < 0.05). Multivariate Cox regression confirmed massive haemorrhage (> 5 L) as an independent mortality predictor ( p  < 0.001). LASSO-selected predictors (prior abdominal surgery, blood loss > 5 L, and ALBI score) formed a prognostic nomogram demonstrating strong discrimination (1-year AUC: 0.824; 2-year AUC: 0.788). Tertile-based stratification revealed significant intergroup differences in survival ( p  < 0.001). Conclusion Massive intraoperative haemorrhage independently predicted post-LT mortality. The validated nomogram integrating surgical history, haemorrhage severity, and ALBI score enables clinically actionable risk stratification, potentially informing perioperative resource allocation and personalised management protocols.