ArticleWorld journal of hepatology2022
Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches.
Article in World journal of hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial intelligence and machine learning in transplantation surgery care pathway.World journal of transplantation · 2026Review
- Persistent Intraoperative Shock and Acute Kidney Injury After Liver Transplantation.Journal of clinical medicine · 2026Article
- The association between potassium level during the anhepatic stage of orthotopic liver transplantation and postoperative acute kidney injury: an exploratory study.BMC nephrology · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Use of machine learning models for the prognostication of liver transplantation: A systematic review.World journal of transplantation · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation. Recently, artificial neural network (ANN) was reported to have better predictive ability than the classical logistic regression (LR) for this postoperative outcome.
aimTo identify the risk factors of AKI after deceased-donor liver transplantation (DDLT) and compare the prediction performance of ANN with that of LR for this complication.
methodsAdult patients with no evidence of end-stage kidney dysfunction (KD) who underwent the first DDLT according to model for end-stage liver disease (MELD) score allocation system was evaluated. AKI was defined according to the International Club of Ascites criteria, and potential predictors of postoperative AKI were identified by LR. The prediction performance of both ANN and LR was tested.
resultsThe incidence of AKI was 60.6% (
conclusionThe severity of liver disease, pre-existing kidney dysfunction, marginal grafts, hemodynamic instability, MBT, and SL are predictors of postoperative AKI, and ANN has better prediction performance than LR in this scenario.
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