ArticleTransplant international : official journal of the European Society for Organ Transplantation2023
A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study.
Article in Transplant international : official journal of the European Society for Organ Transplantation, 2023. 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.
- Machine learning-based nomogram for acute kidney injury after liver transplantation.Renal failure · 2026Article
- Article
- Development of a risk prediction model for acute kidney injury in liver transplant recipients.Frontiers in surgery · 2025Article
- Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.Frontiers in pharmacology · 2025Article
- A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation.BMC nephrology · 2024Article
- 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
- 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
- Intraoperative hypotension during critical phases of liver transplantation and its impact on acute kidney injury: a retrospective cohort study.Brazilian journal of anesthesiology (Elsevier)Article
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7 authors.
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Abstract
Acute kidney injury (AKI) after liver transplantation (LT) is a common complication, and its development is thought to be multifactorial. We aimed to investigate potential risk factors and build a model to identify high-risk patients. A total of 199 LT patients were enrolled and each patient data was collected from the electronic medical records. Our primary outcome was postoperative AKI as diagnosed and classified by the KDIGO criteria. A least absolute shrinkage and selection operating algorithm and multivariate logistic regression were utilized to select factors and construct the model. Discrimination and calibration were used to estimate the model performance. Decision curve analysis (DCA) was applied to assess the clinical application value. Five variables were identified as independent predictors for post-LT AKI, including whole blood serum lymphocyte count, RBC count, serum sodium, insulin dosage and anhepatic phase urine volume. The nomogram model showed excellent discrimination with an AUC of 0.817 (95% CI: 0.758-0.876) in the training set. The DCA showed that at a threshold probability between 1% and 70%, using this model clinically may add more benefit. In conclusion, we developed an easy-to-use tool to calculate the risk of post-LT AKI. This model may help clinicians identify high-risk patients.
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