Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1221
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-30T06:56:19.167802Z
status: published
lens: g_space
sector: health
headline: Machine learning-based nomogram for acute kidney injury after liver transplantation
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: The nomogram achieved area under the receiver operating characteristic curve values of 0.861 in the training cohort and 0.761 in the validation cohort. | This early postoperative model may help identify high-risk recipients for intensified renal monitoring and individualized management. | Among five evaluated models, support vector machine showed the numerically highest validation AUC of 0.846.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers retrospectively developed an early postoperative interpretable nomogram to predict acute kidney injury after liver transplantation using four variables available within about one hour of surgery. In 127 patients split into training and validation cohorts, the model achieved AUCs of 0.861 and 0.761, with a support vector machine variant reaching 0.846 on validation.

Early AKI prediction matters because AKI after liver transplantation is frequent and linked to adverse outcomes, and a tool using routine early labs could trigger intensified renal monitoring. Uncertainty remains because the model has only internal validation in a single-center cohort and the authors state external multicenter validation is required before clinical implementation.
limitation: 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.
tag: Evidence-backed gain
key_points: Retrospective study of 127 adult liver transplant recipients from June 2023 to January 2026, split 3:1 training and validation, defined AKI by KDIGO criteria. | LASSO selected eGFR by CKD-EPI, anhepatic phase time, postoperative natural logarithm-transformed D-dimer, and postoperative alanine aminotransferase; lower eGFR and longer anhepatic time were independently associated with AKI. | Model used variables available by first urgent postoperative blood test generally within 1 h after surgery, with SHAP identifying eGFR as dominant predictor and internal validation by five-fold cross-validation and bootstrap.
rundown: The study screened 146 adult LT recipients and included 127, using perioperative variables available by the first urgent postoperative blood test within 1 hour. LASSO selected four predictors and multivariable logistic regression linked lower eGFR and longer anhepatic phase time to AKI defined by KDIGO.

Performance was reported as AUC 0.861 training and 0.761 validation for the nomogram, with SVM highest at 0.846 among five models, supported by five-fold cross-validation, bootstrap internal validation, and SHAP analysis highlighting eGFR as dominant.
sources:
- peer_reviewed | Renal Failure | https://doi.org/10.1080/0886022x.2026.2698148 | 2026-09-27
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

5d9a62a303edb27d9edef1f67fdbd88b0e96c1e6f0f7705c25c2c45f3ac51b69

previous

0000000000000000000000000000000000000000000000000000000000000000