ArticleRenal failure2021
Early prediction of acute kidney injury after liver transplantation by scoring system and decision tree.
Article in Renal failure, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Intraoperative allogeneic blood transfusion is not associated with postoperative acute kidney injury and in-hospital mortality in liver transplantation patients: a propensity score matching analysis.Frontiers in medicine · 2026Article
- Relationship of intraoperative hypotension with major adverse cardiovascular events and acute kidney injury after pancreaticoduodenectomy.Frontiers in medicine · 2026Article
- Leveraging Large Language Models for Predicting Postoperative Acute Kidney Injury in Elderly Patients.BME frontiers · 2025Article
- Rosiglitazone attenuates Acute Kidney Injury from hepatic ischemia-reperfusion in mice by inhibiting arachidonic acid metabolism through the PPAR-γ/NF-κB pathway.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024Article
- Predictive model of acute kidney injury after spontaneous intracerebral hemorrhage: A multicenter retrospective study.European stroke journal · 2023Article
- Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level.Annals of medicine · 2023Article
- Continuous Renal Replacement Therapy after Liver Transplantation: Peri-Operative Associated Factors and Impact on Survival.Journal of clinical medicine · 2022Article
- "Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation.Journal of hepatology · 2022Review
- Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.Kidney diseases (Basel, Switzerland)Article
- Early Prediction of Acute Kidney Injury Following Liver Transplantation: Development and Validation of a Clinical Risk Model.Journal of clinical and experimental hepatologyArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsEarly detection of acute kidney injury (AKI) is crucial for the prognosis of patients after liver transplantation (LT). This passage aims to analyze the perioperative clinical markers of AKI after LT and establish predictive models based on clinical variables for early detection of AKI after LT.
methodsWe prospectively collected 109 patients with LT, and compared the differences of perioperative clinical markers between the AKI group and non-AKI group. The scoring system and decision tree model were established through the risk factors. Another 163 patients who underwent LT in the same center from 2017 to 2018 were retrospectively collected to verify the models.
resultsIn multiple comparisons of risk factors of post-LT AKI, pre-operative factors were excluded automatically, intraoperative and post-operative factors including operating time, intraoperative hypotension time, post-operative infection, the peak of post-operative AST, and post-operative shock were the independent risk factors for post-LT AKI. The scoring system established with the risk factors has good predictive power (AUC = 0.755) in the validation cohort. The decision tree also shows that post-operative shock was the most important marker, followed by post-operative infection.
conclusionFive intraoperative and post-operative factors are independently associated with post-LT AKI rather than pre-operative factors, which indicates that operation technique and post-operative management may more important for the prevention of post-LT AKI. The scoring system and decision tree model could complement each other, and provide quantitative and intuitive prediction tools for clinical practice of early detection of post-LT AKI.
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