文章摘要
石萍,曹丽,吕德珍.基于全身免疫炎症指数和预后营养指数的列线图模型在肝移植术后急性肾损伤的预测价值探讨[J].安徽医药,2024,28(1):80-84.
基于全身免疫炎症指数和预后营养指数的列线图模型在肝移植术后急性肾损伤的预测价值探讨
Exploring the predictive value of a nomogram model based on the SII and PNI score for acute kidney injury after orthotopic liver transplantation
  
DOI:10.3969/j.issn.1009-6469.2024.01.017
中文关键词: 肝移植  手术后并发症  移植受体  急性肾损伤  列线图  预后营养指数  全身免疫炎症指数
英文关键词: Liver transplantation  Postoperative complications  Transplant recipients  Acute kidney injury  Nomogram  Prog- nostic nutritional index  Systemic immune-inflammation index
基金项目:
作者单位E-mail
石萍 中国人民解放军东部战区总医院秦淮医疗区麻醉科江苏南京 210000  
曹丽 中国人民解放军东部战区总医院秦淮医疗区麻醉科江苏南京 210000  
吕德珍 中国人民解放军东部战区总医院秦淮医疗区麻醉科江苏南京 210000 lvdezhentg@163.com 
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中文摘要:
      目的探讨原位肝移植( orthotopic liver transplantation,OLT)术后发生急性肾损伤( acute kidney injury,AKI)的独立危险因素,并构建列线图预测模型以个性化预测 AKI事件的发生。方法回顾性分析 2017年 3月至 2021年 3月于中国人民解放军东部战区总医院行 OLT手术的终末期肝病病人作为研究对象。入组病人根据 OLT术后 7d内是否发生 AKI事件分为两组。采用单因素、多因素 logistic回归模型分析 AKI的独立危险因素。多因素筛选出的独立危险因素进一步通过 R软件绘制列线图预测模型并评估。结果本研究共纳入 237例 OLT手术病人,其中 101例病人术后 7d内发生了 AKI,发生率为 42.6%。AKI组病人的术前伴随的肝昏迷比例[ 22.8%(23/101)比 9.6%(13/136)]、 APACHEⅡ评分、手术时间[(7.7±1.8)h比( 7.0±1.6)h]、术中出血量[( 3 002±1 606)mL]比( 2 525±1 383)mL]、红细胞输注量、术前总胆红素、国际标准化比值( international normalized ratio, INR)( 1.74±0.26比 1.63±0.23)、肌酐及全身免疫炎症指数( systemic immune-inflammation index,SII)(725±458比 553±355)水平均显著高于非 AKI组( P<0.05)而预后营养指数(prognostic nutritional index,PNI)(41.4±5.3比 45.0±6.1)则显著低于非 AKI组病人( P<0.05)。多因素 logistic回归,分析提示 PNI[OR 95%CI:2.63(1.67,3.87),P=0.005]、 SII[OR 95%CI:2.65(1.18,4.41),P=0.011]、 INR[OR 95%CI:1.73(1.02,3.01)P=0.017]、出血量[ OR 95%CI:2.26(1.16,4.33)P=0.014]、肝昏迷[ OR 95%CI:2.51(1.19,5.21)件的独立危险因素。根据这 5个危险因素的列线图风险模型对于肝移植病P=0.015]是术后7d发生AKI事,构建,人术后 AKI发生,预测价值较好,受试者操作特征( ROC)曲线下面积为 0.73[95%CI:(0.65,0.81)]。结论基于 SII、PNI等因素构建的风险模型可有效预测 OLT病人术后 AKI事件的发生。
英文摘要:
      Objective To investigate the independent risk factors for acute kidney injury (AKI) after orthotopic liver transplantation(OLT) and to construct a nomogram prediction model to predict AKI events.Methods Patients with end-stage liver disease who under-went OLT surgery from March 2017 to March 2021 in the Department of Anesthesiology, General Hospital of Eastern Theater Com-mand of PLA were retrospectively analyzed as study subjects. The enrolled patients were divided into two groups according to whetherAKI occurred within 7 days after OLT. Univariate and multivariate logistic regression models were used to analyze the independent riskfactors for AKI. The screened independent risk factors screened by multiple factors were further predicted by the nomogram predictionmodel and evaluated by R software.Results A total of 237 OLT patients were enrolled in this study, of whom 101 patients developedAKI within 7 days after surgery, with an incidence of 42.6%. The proportion of preoperative hepatic encephalopathy [22.8% (23/101) vs. 9.6% (13/136)], APACHE Ⅱ score, surgery duration [(7.7±1.8) vs. (7.0±1.6) h], intraoperative hemorrhage [(3 002±1 606) vs. (2 525± 1 383) mL], erythrocyte transfusion, preoperative total bilirubin, international normalized ratio (INR) (1.74±0.26 vs.1.63±0.23), creati- nine, and systemic immune-inflammation index (SII) (725±458 vs. 553±355) levels were significantly higher in the AKI group than that in the non-AKI group (P < 0.05), while the prognostic nutritional index (PNI) (41.4±5.3 vs. 45.0±6.1) was significantly lower than that in the non-AKI group (P < 0.05). The multivariate logistic regression analysis suggested that PNI [OR 95%CI: 2.63 (1.67, 3.87), P = 0.005], SII [OR 95%CI: 2.65 (1.18, 4.41), P = 0.011], INR [OR 95%CI: 1.73 (1.02, 3.01), P = 0.017], bleeding [OR 95%CI: 2.26 (1.16, 4.33), P = 0.014], and hepatic coma [OR 95%CI: 2.51 (1.19, 5.21), P = 0.015] were the independent risk factors for the occurrence ofan AKI event at 7 d postoperatively. The nomogram risk model constructed based on these 5 risk factors had a good predictive value for the occurrence of postoperative AKI in OLT patients, with an area under the receiver operating characteristic (ROC) curve of 0.73[95%CI:(0.65,0.81)].Conclusion The nomogram risk model constructed based on the SII, PNI and other factors can effectively predictthe occurrence of postoperative AKI events in OLT patients.
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