aceMachine 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 defin…
An interpretable machine learning nomogram using eGFR, anhepatic phase time, postoperative log D-dimer and ALT available within 1 hour after liver transplantation predicted AKI with AUC 0.861 in training and 0.761 in validation, with SVM reaching 0.846, to enable early identification for intensified renal monitoring.
Model was internally validated only and requires external multicenter validation before routine clinical use, limiting generalizability beyond the single-center retrospective cohort of 127 patients.
Evidence
- Peer-reviewedRenal Failure2026-09-27
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Truvace Impact Record TRV-2026-1221, v1: “Machine learning-based nomogram for acute kidney injury after liver transplantation.” Truvace, 2026-09-30. /record/TRV-2026-1221 (accessed at citation time). sha256 5d9a62a303edb27d…
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