Development and internal validation of a nomogram for early prediction of deep vein thrombosis after liver transplantation: an exploratory machine-learning comparison.

Background: Lower-extremity deep vein thrombosis (DVT) after liver transplantation (LT) may delay recovery and complicate thromboprophylaxis. Practical transplant-specific tools for early risk stratification remain limited. Methods: Adult recipients who underwent LT between August 2023 and January 2026 were retrospectively screened. After exclusions, 131 recipients were divided by outcome-stratified random sampling into a training cohort ( Results: DVT occurred in 26 of 131 recipients (19.8%). The logistic model included postoperative ln (D-dimer), anhepatic phase, postoperative activated partial thromboplastin time, and preoperative prothrombin time. Its AUC was 0.79 (95% CI, 0.67-0.90) in the training cohort and 0.81 (95% CI, 0.56-1.00) in the hold-out subset. The 5-fold cross-validated AUC was 0.76 (95% CI, 0.63-0.86), and the bootstrap optimism-corrected AUC was 0.76. Wide, overlapping confidence intervals and model-specific optimism did not support reliable algorithm ranking. Conclusion: A concise logistic regression-based nomogram using routinely available perioperative variables may support early individualized DVT risk estimation after LT. The machine-learning analyses should be regarded as exploratory. Larger prospective multicenter studies are required before clinical implementation.