A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

被引:8
作者
Zeng, Jianfeng [1 ]
Li, Qiaoyun [2 ]
Wu, Qixing [3 ]
Li, Li [1 ]
Ye, Xijiu [1 ]
Liu, Jing [4 ]
Cao, Bingbing [1 ]
机构
[1] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Anesthesiol, Guangzhou, Peoples R China
[2] Sun Yat Sen Univ, Zhongshan Med Sch, Dept Physiol, Guangzhou, Peoples R China
[3] Univ Sci & Technol China, Dept Anesthesiol, Affiliated Hosp 1, Hefei, Peoples R China
[4] Sun Yat Sen Univ, Affiliated Hosp 8, Dept Anesthesiol, Shenzhen, Peoples R China
关键词
liver transplantation; risk factors; acute kidney injury; nomogram; online calculator; MODEL; DEFINITION; SURVIVAL; EVENTS;
D O I
10.3389/ti.2023.10887
中图分类号
R61 [外科手术学];
学科分类号
摘要
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.
引用
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页数:13
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