桑晓辉 1赵晋明 1白磊 1王智鹏 1李涛 1何翼彪1
作者信息
- 1. 新疆医科大学第一附属医院消化血管外科中心/肝脏·腹腔镜外科,新疆 乌鲁木齐 830054
- 折叠
摘要
Abstract
Objective To establish a prediction model for acute kidney injury(AKI)after liver transplantation based on LASSO regression.Methods The basic information and clinical data of 83 patients undergoing liver transplantation by living donor or donation after circulatory death(DCD)donor in the First Affiliated Hospital of Xinjiang Medical University from Jun.2015 to Jun.2024 were retrospectively collected.According to the AKI diagnostic criteria revised by Kidney Disease Improving Global Outcomes(KDIGO)in 2012,the 83 patients were divided into the AKI group(n=42)and the non-AKI group(n=41).Univariate analysis was used to screen factors associated with AKI after liver transplantation,and further selection was optimized with the least absolute shrinkage and selection operator(LASSO)regression.Additionally multivariate Logistic regression was employed to construct a prediction model and draw a nomogram.The model's differentiation,calibration,and clinical applicability were measured by the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).The Bootstrap method was used for internal verification.Results Univariate analysis showed that,AKI after liver transplantation was significantly correlated with donor type,gender,body mass index(BMI),diabetes,hypertension,anhepatic phase,volume of intraoperative blood loss,intraoperative urine output,peak postoperative AST,and peak postoperative ALT(P<0.05).LASSO regression identified 4 independent predictors:BMI,anhepatic phase,intraoperative urine output,and peak postoperative AST.Multivariate Logistic regression showed that,BMI≥24.0 kg/m2(OR=14.209,95%CI 3.716 to 76.010),anhepatic phase≥120.0 min(OR=4.746,95%CI 1.240 to 22.270),intraoperative urine output≤3 710.0 mL(OR=6.238,95%CI 1.694 to 27.840)and peak postoperative AST≥1 476.0 U/L(OR=12.252,95%CI 3.260 to 63.560)were independent risk factors for AKI after liver transplantation(P<0.05).According to the above 4 factors,a nomogram prediction model was constructed,and the area under the curve(AUC)was 0.893(95%CI 0.828 to 0.959).Calibration curve showed that the predicted results of the model fit well with the actual results.Spiegelhalter Z test showed P=0.873,and DCA results showed that the model had a high net benefit in predicting AKI after liver transplantation.Conclusion The model constructed in this study for predicting AKI after liver transplantation exhibits strong reliability and clinical applicability.关键词
肝移植/急性肾损伤/危险因素/列线图/预测模型Key words
liver transplantation/acute kidney injury/risk factors/nomogram/prediction model分类
临床医学
