乳酸水平对HBV相关慢加急性肝衰竭患者短期预后的预测价值
DOI: 10.3969/j.issn.1001-5256.2022.07.007
Value of lactate level in predicting the short-term prognosis of patients with acute-on-chronic hepatitis B liver failure
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摘要:
目的 本研究旨在评估乳酸对HBV相关慢加急性肝衰竭(HBV-ALCF)患者90 d预后的预测价值。 方法 回顾性纳入2017年1月—2021年6月新疆医科大学第一附属医院感染病·肝病中心收治的117例HBV-ACLF患者,根据90 d随访结果分为生存组(n=43)和死亡组(n=74);收集患者于本院诊断HBV-ACLF后24 h内的血常规、肝肾功能、凝血功能、动脉血气、肝性脑病等并发症,计算ALBI、MELD、MELD-Na、MESO、iMELD、CLIF-C ACLF评分。计量资料符合正态分布的组间比较采用t检验,非正态分布的组间比较采用Mann-Whitney U检验,计数资料组间比较采用χ2检验。采用二元logistic分析乳酸是否为HBV-ACLF患者90 d预后的独立危险因素,Pearson相关分析检验乳酸与感染情况及临床并发症的相关性。绘制受试者工作特征曲线(ROC曲线)分析乳酸相加于各评分后能否提高现有模型的预测价值,两两比较采用Z检验。 结果 死亡组WBC、INR、乳酸均大于生存组,Na小于生存组,差异均有统计学意义(Z值分别为-2.813、-2.855、-3.186、-2.044,P值均<0.05);年龄、MELD、MELD-Na、MESO、iMELD、CLIF-C ACLF评分均显著高于生存组,差异均有统计学意义(t值分别为2.536、4.615、4.247、4.941、5.832、7.562,P值均<0.05)。logistic分析表明年龄(OR=1.075,95%CI: 1.026~1.127,P=0.002)、INR(OR=2.198,95%CI: 1.149~4.203,P=0.017)、乳酸(OR=1.431,95%CI: 1.002~2.044,P=0.049)是HBV-ACLF患者90 d预后的独立危险因素,将乳酸纳入现有模型后ROC曲线下面积均有所增加。 结论 乳酸是HBV-ACLF患者90 d预后的独立危险因素,将其纳入现有模型可能提高预测价值。 Abstract:Objective To investigate the value of lactate in predicting the 90-day prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). Methods A retrospective analysis was performed for 117 patients with HBV-ACLF who were admitted to Center for Infectious Diseases-Hepatology, The First Affiliated Hospital of Xinjiang Medical University, from January 2017 to June 2021, and according to the results of 90-day follow-up, they were divided into survival group with 43 patients and death group with 74 patients. Related clinical data were collected, including routine blood test results, hepatic and renal function, coagulation function, arterial blood gas parameters, and complications such as hepatic encephalopathy within 24 hours after the diagnosis of HBV-ACLF in our hospital, and albumin-bilirubin (ALBI), Model for End-Stage Liver Disease (MELD), MELD combined with serum sodium concentration (MELD-Na), MELD to SNa ratio (MESO), integrated MELD (iMELD), and CLIF-C ACLF scores were calculated. The t-test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data between groups. A binary logistic regression analysis was used to investigate whether lactate was an independent risk factor for the 90-d prognosis of HBV-ACLF patients, and a Pearson correlation analysis was used to investigate the correlation of lactate with infection and clinical complications. The receiver operating characteristic (ROC) curve was plotted to analyze whether lactate, in combination with each score, can improve the predictive value of the existing model, and the Z test was used for pairwise comparison. Results Compared with the survival group, the death group had significantly higher white blood cell count, international normalized ratio (INR), and lactate and a significantly lower level of Na (Z=-2.813, -2.855, and -3.186, and -2.044, all P < 0.05), as well as significantly higher age and MELD, MELD-Na, MESO, iMELD, and CLIF-C ACLF scores (t=2.536, 4.615, 4.247, 4.941, 5.832, and 7.562, all P < 0.05). The logistic regression analysis showed that age (odds ratio [OR]=1.075, 95% confidence interval [CI]: 1.026-1.127, P=0.002), INR (OR=2.198, 95%CI: 1.149-4.203, P=0.017), and lactate (OR=1.431, 95%CI: 1.002-2.044, P=0.049) were independent risk factors for the 90-day prognosis of patients with HBV-ACLF, and there was an increase in the area under the ROC curve after lactate was included in the existing models. Conclusion Lactate is an independent risk factor for the 90-day prognosis of patients with HBV-ACLF, and its inclusion in existing models may improve predictive value. -
Key words:
- Acute-on-Chronic Liver Failure /
- Hepatitis B virus /
- Lactic Acid /
- Prognosis
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表 1 CLIF-C OF评分
Table 1. CLIF-C OF scores
器官/系统 1分 2分 3分 肝脏(TBil, mg/dL) <6 6~12 >12 肾脏(Cr, mg/dL) <2 2~3.4 ≥3.5或肾脏替代治疗 凝血功能(INR) <2 2~2.4 ≥2.5 神经(感性脑病分级) 0 Ⅰ~Ⅱ Ⅲ~Ⅳ 循环(平均动脉压,mmHg) ≥70 <70 应用血管活性药物
(去甲肾上腺素、多巴胺、肾上腺素)呼吸 PaO2/FiO2 >300 201~300 ≤200 SPO2/FiO2 >357 215~357 ≤214 注:Cr,肌酐;PaO2/FiO2,氧分压/氧浓度;SpO2/FiO2,血氧饱和度/氧浓度。 表 2 入组时基线资料的比较分析
Table 2. Comparative analysis of baseline information at enrollment
指标 生存组(n=43) 死亡组(n=74) 统计值 P值 年龄(岁) 44.05±10.32 49.70±12.33 t=2.536 0.013 男/女(例) 31/12 55/19 χ2=0.070 0.792 WBC(×109/L) 6.25(5.10~8.20) 7.83(5.90~11.57) Z=-2.813 0.005 红细胞分布宽度(%) 16.20(14.60~17.80) 17.45(15.28~18.60) Z=-1.804 0.071 Na(mmol/L) 135.00(132.42~136.87) 132.96(128.42~136.0) Z=-2.044 0.041 血小板压积(%) 0.56(0.31~0.90) 0.59(0.40~1.02) Z=-0.933 0.351 PLT(×109/L) 122(90~142) 91(60~133.5) Z=-1.883 0.060 Alb(g/L) 29.29(24.81~32.57) 28.22(25.54~31.42) Z=-0.334 0.739 INR 2.52(1.96~3.03) 2.85(2.43~3.59) Z=-2.855 0.004 Cr(mg/dL) 0.72(0.62~0.88) 0.84(0.62~1.16) Z=-1.916 0.055 TBil(mg/dL) 17.08(13.46~20.58) 19.67(14.12~26.51) Z=-1.713 0.087 AST(U/L) 222.72(111.14~563.52) 215.95(105.59~529.12) Z=-0.636 0.525 ALT(U/L) 362.10(161.57~682.10) 197.87(64.42~648.65) Z=-1.269 0.204 乳酸(mmol/L) 2.30(1.80~2.70) 2.60(2.00~3.82) Z=-3.186 0.001 ALBI评分 -1.30(-1.59~-0.94) -1.16(-1.48~-0.92) Z=-1.198 0.231 MELD评分 24.29±4.27 28.99±6.74 t=4.615 <0.001 MELD-Na评分 25.66±8.33 34.28±13.63 t=4.247 <0.001 MESO评分 1.81±0.34 2.21±0.53 t=4.941 <0.001 iMELD评分 43.61±5.69 51.73±9.37 t=5.832 <0.001 CLIF-C ACLF评分 45.48±6.71 56.70±9.22 t=7.562 <0.001 糖尿病[例(%)] 3(6.98) 14(18.92) χ2=3.123 0.077 肝硬化[例(%)] 25(58.14) 52(70.27) χ2=1.779 0.182 人工肝支持[例(%)] 26(60.47) 42(56.76) χ2=0.154 0.695 表 3 两组并发症发生情况比较
Table 3. Comparison of the occurrence of complications between the two groups
指标 例数 生存组(n=43) 死亡组(n=74) χ2值 P值 上消化道出血[例(%)] 15 1(2.33) 14(18.92) 6.700 0.010 肺部感染或感染性休克[例(%)] 41 8(18.60) 33(44.59) 8.070 0.004 肝肾综合征[例(%)] 13 0 13(17.57) 6.813 0.009 腹水[例(%)] 98 34(79.07) 64(86.49) 1.100 0.294 肝性脑病[例(%)] 51 6(13.95) 45(60.81) 24.284 <0.001 表 4 HBV-ACLF患者预后影响因素的多因素logistic分析
Table 4. Multi-factor logistic analysis of the prognostic factors of HBV-ACLF
指标 B值 SE Wald P值 OR 95%CI 年龄 0.073 0.024 9.174 0.002 1.075 1.026~1.127 INR 0.787 0.331 5.667 0.017 2.198 1.149~4.203 乳酸 0.359 0.182 3.888 0.049 1.431 1.002~2.044 表 5 各模型预测90 d病死率的的准确性
Table 5. Accuracy of each model in predicting 90-d mortality rate
评分模型 AUC 95%CI P值 cut-off值 灵敏度 特异度 ALBI 0.567 0.458~0.675 0.231 -1.233 9 0.595 0.558 乳酸 0.677 0.579~0.775 0.001 3.150 0 0.405 0.930 MELD 0.698 0.604~0.791 <0.001 29.574 6 0.432 0.953 MELD-Na 0.698 0.603~0.793 <0.001 31.935 7 0.527 0.837 MESO 0.709 0.617~0.802 <0.001 2.286 9 0.446 0.977 iMELD 0.766 0.682~0.851 <0.001 50.144 4 0.527 0.907 CLIF-C ACLF 0.832 0.761~0.904 <0.001 51.472 6 0.689 0.860 表 6 相加乳酸后六种新模型的预测价值
Table 6. Predictive values of the six new models after summing lactate
评分模型 AUC 95%CI P值 灵敏度 特异度 ALBI+乳酸 0.690 0.593~0.787 0.001 0.541 0.767 MELD+乳酸 0.709 0.617~0.801 <0.001 0.446 0.977 MELD-Na+乳酸 0.717 0.625~0.809 <0.001 0.622 0.767 MESO+乳酸 0.719 0.625~0.813 <0.001 0.446 0.907 iMELD+乳酸 0.775 0.692~0.858 <0.001 0.514 0.907 CLIF-C ACLF+乳酸 0.839 0.769~0.909 <0.001 0.622 0.953 -
[1] BERNAL W, JALAN R, QUAGLIA A, et al. Acute-on-chronic liver failure[J]. Lancet, 2015, 386(10003): 1576-1587. DOI: 10.1016/S0140-6736(15)00309-8. [2] LIAW YF. Antiviral therapy of chronic hepatitis B: opportunities and challenges in Asia[J]. J Hepatol, 2009, 51(2): 403-410. DOI: 10.1016/j.jhep.2009.04.003. [3] SHEN Y, WANG X, ZHANG S, et al. A comprehensive validation of HBV-related acute-on-chronic liver failure models to assist decision-making in targeted therapeutics[J]. Sci Rep, 2016, 6: 33389. DOI: 10.1038/srep33389. [4] CHEN W, YOU J, CHEN J, et al. Combining the serum lactic acid level and the lactate clearance rate into the CLIF-SOFA score for evaluating the short-term prognosis of HBV-related ACLF patients[J]. Expert Rev Gastroenterol Hepatol, 2020, 14(6): 483-489. DOI: 10.1080/17474124.2020.1763172. [5] CHEN W, YOU J, CHEN J, et al. Modified model for end-stage liver disease improves short-term prognosis of hepatitis B virus-related acute-on-chronic liver failure[J]. World J Gastroenterol, 2017, 23(40): 7303-7309. DOI: 10.3748/wjg.v23.i40.7303. [6] van HALL G. Lactate kinetics in human tissues at rest and during exercise[J]. Acta Physiol(Oxf), 2010, 199(4): 499-508. DOI: 10.1111/j.1748-1716.2010.02122.x. [7] NIE Y, ZHANG Y, LIU LX, et al. Serum lactate level predicts short-term and long-term mortality of HBV-ACLF patients: a prospective study[J]. Ther Clin Risk Manag, 2020, 16: 849-860. DOI: 10.2147/TCRM.S272463. [8] Liver Failure and Artificial Liver Group, Chinese Society of Infectious Diseases, Chinese Medical Association; Severe Liver Disease and Artificial Liver Group, Chinese Society of Hepatology, Chinese Medical Association. Guideline for diagnosis and treatment of liver failure(2018)[J]. J Clin Hepatol, 2019, 35(1): 38-44. DOI: 10.3969/j.issn.1001-5256.2019.01.007.中华医学会感染病学分会肝衰竭与人工肝学组, 中华医学会肝病学分会重型肝病与人工肝学组. 肝衰竭诊治指南(2018年版)[J]. 临床肝胆病杂志, 2019, 35(1): 38-44. DOI: 10.3969/j.issn.1001-5256.2019.01.007. [9] JOHNSON PJ, BERHANE S, KAGEBAYASHI C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade[J]. J Clin Oncol, 2015, 33(6): 550-558. DOI: 10.1200/JCO.2014.57.9151. [10] KAMATH PS, WIESNER RH, MALINCHOC M, et al. A model to predict survival in patients with end-stage liver disease[J]. Hepatology, 2001, 33(2): 464-470. DOI: 10.1053/jhep.2001.22172. [11] BIGGINS SW, KIM WR, TERRAULT NA, et al. Evidence-based incorporation of serum sodium concentration into MELD[J]. Gastroenterology, 2006, 130(6): 1652-1660. DOI: 10.1053/j.gastro.2006.02.010. [12] HUO TI, WANG YW, YANG YY, et al. Model for end-stage liver disease score to serum sodium ratio index as a prognostic predictor and its correlation with portal pressure in patients with liver cirrhosis[J]. Liver Int, 2007, 27(4): 498-506. DOI: 10.1111/j.1478-3231.2007.01445.x. [13] LUCA A, ANGERMAYR B, BERTOLINI G, et al. An integrated MELD model including serum sodium and age improves the prediction of early mortality in patients with cirrhosis[J]. Liver Transpl, 2007, 13(8): 1174-1180. DOI: 10.1002/lt.21197. [14] HERNAEZ R, SOLÀ E, MOREAU R, et al. Acute-on-chronic liver failure: an update[J]. Gut, 2017, 66(3): 541-553. DOI: 10.1136/gutjnl-2016-312670. [15] REN Y, LIU L, LI Y, et al. Development and validation of a scoring system to predict progression to acute-on-chronic liver failure in patients with acute exacerbation of chronic hepatitis B[J]. Hepatol Res, 2018, 48(9): 692-700. DOI: 10.1111/hepr.13062. [16] ZHANG WJ, ZHANG LJ, WU JZ. Value of MELD、AARC、COSSH ccoring systems in evaluating the 90 -day prognosis of hepatitis B virus-related acute-on-chronic liver failure[J]. J Clin Hepatol, 2020, 36(4): 813-817. DOI: 10.3969/j.issn.1001-5256.2020.04.021.张文佳, 赵丽娟, 吴基洲. MELD、AARC、COSSH评分系统对乙型肝炎相关慢加急性肝衰竭90天预后的评估价值[J]. 临床肝胆病杂志, 2020, 36(4): 813-817. DOI: 10.3969/j.issn.1001-5256.2020.04.021. [17] DU Q, LU SJ, LU DX, et al. Clinical characteristics and prognosis of patients with decompensated liver cirrhosis complicated with chronic and acute liver failure[J]. Chin J Gerontol, 2021, 41(3): 520-523. DOI: 10.3969/j.issn.1005-9202.2021.03.021.杜蔷, 陆盛菊, 卢大秀, 等. HBV感染失代偿期肝硬化并发慢加急性肝衰竭患者的临床特点及预后[J]. 中国老年学杂志, 2021, 41(3): 520-523. DOI: 10.3969/j.issn.1005-9202.2021.03.021. [18] SHANG DB, XIANG XG. Advances in the pathogenesis and treatment of acute-on-chronic liver failure[J]. J Clin Hepatol, 2021, 37(4): 765-769. DOI: 10.3969/j.issn.1001-5256.2021.04.005.尚大宝, 项晓刚. 慢加急性肝衰竭的发病机制和治疗进展[J]. 临床肝胆病杂志, 2021, 37(4): 765-769. DOI: 10.3969/j.issn.1001-5256.2021.04.005. [19] KRAUT JA, MADIAS NE. Lactic acidosis[J]. N Engl J Med, 2014, 371(24): 2309-2319. DOI: 10.1056/NEJMra1309483. [20] HILL AV. Muscular activity and carbohydrate metabolism[J]. Science, 1924, 60(1562): 505-514. DOI: 10.1126/science.60.1562.505. [21] BROOKS GA. The science and translation of lactate shuttle theory[J]. Cell Metab, 2018, 27(4): 757-785. DOI: 10.1016/j.cmet.2018.03.008. [22] CARDOSO FS, ABRALDES JG, SY E, et al. Lactate and number of organ failures predict intensive care unit mortality in patients with acute-on-chronic liver failure[J]. Liver Int, 2019, 39(7): 1271-1280. DOI: 10.1111/liv.14083. [23] KRUSE JA, ZAIDI SA, CARLSON RW. Significance of blood lactate levels in critically ill patients with liver disease[J]. Am J Med, 1987, 83(1): 77-82. DOI: 10.1016/0002-9343(87)90500-6. [24] JEPPESEN JB, MORTENSEN C, BENDTSEN F, et al. Lactate metabolism in chronic liver disease[J]. Scand J Clin Lab Invest, 2013, 73(4): 293-299. DOI: 10.3109/00365513.2013.773591. [25] DROLZ A, HORVATITS T, RUTTER K, et al. Lactate improves prediction of short-term mortality in critically ill patients with cirrhosis: a multinational study[J]. Hepatology, 2019, 69(1): 258-269. DOI: 10.1002/hep.30151. [26] CHAMULEAU RA. Bioartificial liver support anno 2001[J]. Metab Brain Dis, 2002, 17(4): 485-491. DOI: 10.1023/a:1021990725508. [27] TRIANTAFYLLOU E, WOOLLARD KJ, MCPHAIL M, et al. The role of monocytes and macrophages in acute and acute-on-chronic liver failure[J]. Front Immunol, 2018, 9: 2948. DOI: 10.3389/fimmu.2018.02948. [28] ZHANG DJ, ZHOU B, HOU JL. Research progress in prognostic models of acute-on-chronic liver failure[J]. J Clini Hepatol, 2018, 34(6): 1351-1356. DOI: 10.3969/j.issn.1001-5256.2018.06.047.张东敬, 周彬, 侯金林. 慢加急性肝衰竭预后模型的研究进展[J]. 临床肝胆病杂志, 2018, 34(6): 1351-1356. DOI: 10.3969/j.issn.1001-5256.2018.06.047. [29] LI H, ZHENG J, CHEN L, et al. The scoring systems in predicting short-term outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure[J]. Ann Palliat Med, 2020, 9(5): 3048-3058. DOI: 10.21037/apm-20-608. [30] WILLIAMS MA, BEVAN MJ. Effector and memory CTL differentiation[J]. Annu Rev Immunol, 2007, 25: 171-192. DOI: 10.1146/annurev.immunol.25.022106.141548. [31] WHERRY EJ, AHMED R. Memory CD8 T-cell differentiation during viral infection[J]. J Virol, 2004, 78(11): 5535-5545. DOI: 10.1128/JVI.78.11.5535-5545.2004. [32] MICHALEK RD, RATHMELL JC. The metabolic life and times of a T-cell[J]. Immunol Rev, 2010, 236: 190-202. DOI: 10.1111/j.1600-065X.2010.00911.x. [33] JACOBS SR, HERMAN CE, MACIVER NJ, et al. Glucose uptake is limiting in T cell activation and requires CD28-mediated Akt-dependent and independent pathways[J]. J Immunol, 2008, 180(7): 4476-4486. DOI: 10.4049/jimmunol.180.7.4476. [34] RATHMELL JC, ELSTROM RL, CINALLI RM, et al. Activated Akt promotes increased resting T cell size, CD28-independent T cell growth, and development of autoimmunity and lymphoma[J]. Eur J Immunol, 2003, 33(8): 2223-2232. DOI: 10.1002/eji.200324048. [35] VELLINGA N, BOERMA EC, KOOPMANS M, et al. Mildly elevated lactate levels are associated with microcirculatory flow abnormalities and increased mortality: a microSOAP post hoc analysis[J]. Crit Care, 2017, 21(1): 255. DOI: 10.1186/s13054-017-1842-7. [36] BAKKER J, NIJSTEN MW, JANSEN TC. Clinical use of lactate monitoring in critically ill patients[J]. Ann Intensive Care, 2013, 3(1): 12. DOI: 10.1186/2110-5820-3-12. [37] JEPPESEN JB, MORTENSEN C, BENDTSEN F, et al. Lactate metabolism in chronic liver disease[J]. Scand J Clin Lab Invest, 2013, 73(4): 293-299. DOI: 10.3109/00365513.2013.773591. [38] BOSOI CR, ROSE CF. Elevated cerebral lactate: Implications in the pathogenesis of hepatic encephalopathy[J]. Metab Brain Dis, 2014, 29(4): 919-925. DOI: 10.1007/s11011-014-9573-9.