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重症酒精性肝炎进展为慢加急性肝衰竭的危险因素
 发布时间:2022/7/16 浏览次数:206

重症酒精性肝炎进展为慢加急性肝衰竭的危险因素
作者:王浩宇1 2  全卉1 2  江宇泳1  杨玉英1  于浩1  孟培培1 
单位:1.首都医科大学附属北京地坛医院 中西医结合中心 北京 100015 2.北京中医药大学东直门医院 北京 100700 
关键词:重症酒精性肝炎 肝衰竭 危险因素 中性粒细胞/淋巴细胞比值 高密度脂蛋白胆固醇 贫血 
分类号:
出版年,卷(期):页码:2022,14(1):34-41
摘要:
摘要:目的 探讨重症酒精性肝炎(severe alcoholic hepatitis,SAH)患者住院期间发生 慢加急性肝衰竭(acute-on-chronic liver failure,ACLF)的危险因素。方法 回顾性收集 2008年8月至2017年10月首都医科大学附属北京地坛医院诊断为SAH的258例住院患者为 研究对象,根据住院期间是否发生ACLF分为ACLF组(84例)和无ACLF组(174例), 比较两组患者的临床资料、血常规 [白细胞总数(white blood cell,WBC)、血小 板(platelet,PLT)、血红蛋白(hemoglobin,HGB)、红细胞平均体积(mean corpuscular volume,MCV)]、肝功能 [丙氨酸氨基转移酶(alanine aminotransferase, ALT)、天门冬氨酸氨基转移酶(aspartate aminotransferase,AST)、总胆红素 (total bilirubin,TBil)、γ-谷氨酰转移酶(gamma-glutamyl transpeptidase,GGT)、 白蛋白(albumin,ALB)]、肾功能 [血清肌酐(creatinine,Cr)]、血脂 [总胆固醇 (totalcholesterol,TCHO)、甘油三酯(triglyceride,TG)、高密度脂蛋白胆固醇 (high density lipoproteins cholesterol,HDL-C),低密度脂蛋白胆固醇(low density lipoproteins cholesterol,LDL-C)]、血Na+ 、血糖(glucose,Glu)、甲胎蛋白(alphafetoprotein,AFP)、国际标准化比值(international standard ratio,INR),中性粒细 胞/淋巴细胞比值(neutrophil lymphocyte ratio,NLR)、Maddery判别函数(Maddrey discriminant function,MDF)评分、Child-Turcotte-Pugh(CTP)评分、终末期肝病模 型(model for end-stage liver disease,MELD)评分和并发症如腹水、上消化道出血、 肝性脑病、高血压和糖尿病等,采用Cox回归分析SAH患者进展为ACLF的危险因素并 构建危险指数(PI)模型。使用受试者特征(receiver operating characteristic,ROC) 曲线对新模型进行评价。结果 ACLF组患者的住院时间(中位数:31.5 d vs 20.0 d)、 MDF评分(中位数:51.3分vs 46.5分)、MELD评分(中位数:13.8分vs 11.1分)、 TBil(中位数:195.6 μmol/L vs 129.5 μmol/L)、WBC(中位数:7.8 × 1012/L vs 5.7 × 1012/L)、NLR(中位数:6.0 vs 3.8)及Cr(中位数:75.1 μmol/L vs 62.0 μmol/L)均显 著高于无ACLF组患者,HDL-C(中位数:0.2 mmol/L vs 0.3 mmol/L)、LDL-C(中 位数:1.0 mmol/L vs 1.2 mmol/L)、HGB [(91.6 ± 24.3)g/L vs(98.0 ± 22.9)g/L] 和Na+ (中位数:133.0 mmol/L vs 135.3 mmol/L)均显著低于无ACLF组患者(P均< 0.05)。 ACLF组患者腹水 [94.0%(79/84)vs 82.8%(144/174);χ 2 = 6.157,P = 0.013] 和合 并感染 [85.7%(72/84)vs 73.6%(128/174);χ 2 = 4.800,P = 0.028] 发生率显著高于 无ACLF组。Cox多因素回归分析表明TBil(HR = 1.648,95%CI:1.076~2.524,P = 0.022)、Cr(HR = 2.226,95%CI:1.119~4.426,P = 0.023)和NLR(HR = 1.466, 95%CI:1.039~2.067,P = 0.029)是SAH进展为ACLF的独立危险因素,而HDL-C (HR = 0.688,95%CI:0.520~0.910,P = 0.009)和HGB(HR = 0.217,95%CI: 0.089~0.529,P = 0.001)是保护因素。PI = 0.382 × LnNLR + 0.800 × LnCr + 0.500 × LnTBil - 0.374 × LnHDL-C - 1.527 × LnHGB,PI模型预测SAH患者住院期间发生ACLF的 ROC曲线下面积为0.722,高于常用的CTP评分(0.533;z = 3.847,P < 0.001)和MDF 评分(0.601;z = 2.323,P = 0.02),与MELD评分的ROC曲线下面积差异无统计学意义 (0.622;z = 1.261,P = 0.21)。PI模型ROC曲线的截断值为0.36,将PI ≥ 0.36定义为高 风险组,患者进展为ACLF的中位时间为10.50 d,ACLF发生率为53.6%;PI < 0.36为低 风险组,患者进展为ACLF的中位时间为29.50 d,ACLF发生率为22.7%,两组SAH患者 ACLF发生率差异有统计学意义(Log rank χ 2 = 26.13,P < 0.001)。结论 本研究建立的 PI模型可预测SAH进展为ACLF的风险,有利于临床医生早期识别病情进展的SAH患者。
Abstract: Objective To investigate the risk factors of severe alcoholic hepatitis (SAH) progressing to acute-on-chronic liver failure (ACLF). Methods A total of 258 patients with SAH in Beijing Ditan Hospital, Capital Medical University from August 2008 to October 2017 were retrospectively selected and divided into ACLF group (84 cases) and non-ACLF group (174 cases) according to whether ACLF occurred during hospitalization. Clinical data, blood routine indexes [white blood cell (WBC), platelet (PLT), hemoglobin (HGB), mean corpuscular volume(MCV)], liver function [alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBil), gamma-glutamyl transpeptidase (GGT), albumin (ALB)], renal function [creatinine (Cr)], blood fat indexes [total cholesterol (TCHO), triglyceride (TG), high density lipoproteins cholesterol (HDL-C), low density lipoproteins cholesterol (LDL-C)], Na+ , glucose (Glu), alpha-fetoprotein (AFP), international standard ratio (INR), neutrophil lymphocyte ratio (NLR), MDF, Child-Turcotte-Pugh (CTP) score, model for end-stage liver disease (MELD) score and complications such as ascites, gastrointestinal bleeding and hepatic encephalopathy of patients in two groups were compared. Cox regression analysis was used to analyze the risk factors of SAH progressing to ACLF and construct the PI model. The evaluation of new model was performed by receiver operating characteristic (ROC) curve. Results Hospital stay (median: 31.5 d vs 20.0 d), MDF score (median: 51.3 points vs 46.5 points), MELD score (median: 13.8 points vs 11.1 points), TBil (median: 195.6 μmol/L vs 129.5 μmol/L), WBC (median: 7.8 × 1012/L vs 5.7 × 1012/L), NLR (median: 6.0 vs 3.8) and Cr (median: 75.1 μmol/L vs 62.0 μmol/L) of patients in ACLF group were significantly higher than those of non-ACLF group, HDL-C (median: 0.2 mmol/L vs 0.3 mmol/L), LDL-C (median: 1.0 mmol/L vs 1.2 mmol/L), HGB [(91.6 ± 24.3) g/L vs (98.0 ± 22.9)g/L] and Na+ (median: 133.0 mmol/L vs 135.3 mmol/L) were significantly lower than those of non-ACLF group (all P < 0.05). The incidence of ascites [94.0% (79/84) vs 82.8% (144/174); χ 2 = 6.157, P = 0.013] and infection [85.7% (72/84) vs 73.6% (128/174); χ 2 = 4.800, P = 0.028] of patients in ACLF group were significantly higher than those of non-ACLF group. Cox regression analysis showed that TBil (HR = 1.648, 95%CI: 1.076~2.524, P = 0.022), Cr (HR = 2.226, 95%CI: 1.119~4.426, P = 0.023) and NLR (HR = 1.466, 95%CI: 1.039~2.067,P = 0.029) were risk factors of SAH progressing to ACLF, HDL-C (HR = 0.688, 95%CI: 0.520~0.910, P = 0.009) and HGB (HR = 0.217, 95%CI: 0.089~0.529, P = 0.001) were protective factors. PI = 0.382 × LnNLR + 0.800 × LnCr + 0.500 × LnTBil - 0.374 × LnHDL-C - 1.527 × LnHGB. The area under ROC curve of PI model predicting SAH progressing to ACLF during hospitalization was 0.722, which was higher than that of CTP score (0.533; z = 3.847, P < 0.001) and MDF score (0.601, z = 2.323, P = 0.02), and there was no significant difference compared with the area under the ROC curve of MELD score (0.622; z = 1.261, P = 0.21). The cutoff value of ROC curve of PI model was 0.36, PI ≥ 0.36 was defined as high-risk group, the median time of the patients progressing to ACLF was 10.50 d, the incidence rate of ACLF was 53.6%; PI < 0.36 was defined as low-risk group, the median time of the patients progressing to ACLF was 29.50 d, the incidence rate of ACLF was 22.7%. The difference of ACLF incidence of patients in both group were statistically significant (Log rank χ 2 = 26.13, P < 0.001). Conclusions PI model established in this study can predict the risk of SAH progression to ACLF, and each indicator can be obtained by blood routine and biochemical examination, enabling clinicians to early identify patients with progressive SAH.
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