Establishment of a predictive model of death within 30 days for patients with liver cirrhosis and bacterial ascites

Huang YunYi; Wang XianBo

doi:10.3969/j.issn.1001-5256.2019.11.017

Volume 35 Issue 11

Nov. 2019

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Article Navigation > Journal of Clinical Hepatology > 2019 > 35(11): 2472-2477

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Establishment of a predictive model of death within 30 days for patients with liver cirrhosis and bacterial ascites

DOI: 10.3969/j.issn.1001-5256.2019.11.017

Dongzhimen Hospital,Beijing University of Chinese Medicine

Research funding:

Received Date: 2019-07-02
Published Date: 2019-11-20

Abstract

Abstract

Objective To investigate the risk factors for death within 30 days in patients with liver cirrhosis and bacterial ascites,and to establish a predictive model of death within 30 days. Methods A retrospective analysis was performed for the clinical data of 86 patients with liver cirrhosis and bacterial ascites who were admitted to Beijing Ditan Hospital,Capital Medical University,from January 2012 to April 2018.The patients were followed up for 30 days,and according to their prognosis,they were divided into survival group with 73 patients and death group with 13 patients. 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 logistic regression analysis was used to investigate the influencing factors for death within 30 days in patients with liver cirrhosis and bacterial ascites,and a predictive model was established based on these influencing factors. The area under the receiver operating characteristic curve was used to evaluate the predictive value of each independent influencing factor and the predictive model. Results The multivariate logistic regression analysis showed that there were significant differences between the survival group and the death group in ascites albumin( odds ratio [OR]= 0. 615,95% confidence interval [CI]: 0. 424-0. 893,P = 0. 011),neutrophil-to-lymphocyte ratio( NLR)( OR =1. 170,95% CI: 1. 011-1. 354,P = 0. 035),and Model for End-Stage Liver Disease( MELD) score( OR = 1. 341,95%CI: 1. 111-1. 618,P = 0. 002). A scoring model was established based on the results of the multivariate analysis to predict death within 30 days in patients with liver cirrhosis and bacterial ascites,and based on this model,the patients were divided into high-risk group with death within 30 days( score ≥2 points) and low-risk group with death within 30 days( score < 2 points). There was a significant difference in mortality rate with 30 days between the two groups( 60. 0% vs 5. 6%,P < 0. 001). Conclusion Ascites albumin ≤3. 5 g/L,NLR ≥6. 5,and MELD score ≥20 are independent risk factors for death within 30 days in patients with liver cirrhosis and bacterial ascites. The predictive model established on this basis can effectively evaluate the population at high risk of death within 30 days.
- liver cirrhosis,
- bacterial ascites,
- peritonitis,
- prognosis,
- logistic models

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慢性乙型肝炎(CHB)相关的慢加急性肝衰竭(acute-on-chronic hepatitis B liver failure，ACHBLF)是在慢性HBV感染引起的CHB基础上出现的急性严重肝功能障碍临床综合征，病死率极高。因我国慢性HBV的高感染率，ACHBLF已成为影响患者生存质量的重要因素^[1]。在CHB向ACHBLF进展过程中，存在着患者肝功能急剧恶化，但尚未达到肝衰竭的“肝衰竭前期(pre-ACHBLF)”阶段^[2]，如能在此阶段进行预警及干预，则有可能预防进一步发展为肝衰竭。

目前普遍认为细胞免疫功能紊乱是ACHBLF发生的病理机制之一，许多免疫细胞如髓系抑制性细胞(myeloid-derived suppressor cells, MDSC)、调节性T淋巴细胞(Treg)、分泌IL-17的CD4 T淋巴细胞(IL-17-producing CD4 T cells，Th17)和细胞毒性T淋巴细胞等在肝衰竭的发病中发挥重要作用^[3-5]。尽管既往许多研究已证实肝衰竭发病与免疫密切相关，但pre-ACHBLF阶段的免疫状态及其与疾病进展的关系尚不清楚，因此本研究探讨了MDSC、Th17、Treg和分泌IL-17的CD8 T淋巴细胞(IL-17-producing CD8 T cells, Tc17)在pre-ACHBLF和ACHBLF患者中的表达，以期为ACHBLF的早期治疗提供思路。

1. 资料与方法

1.1 研究对象

选取2018年8月—2019年5月于石家庄市第五医院住院患者45例，其中ACHBLF患者、pre-ACHBLF患者和CHB患者各15例，同时选取于本院健康体检者15例作为健康对照组(HC组)。ACHBLF和pre-ACHBLF的临床诊断均符合《肝衰竭诊治指南(2018年版)》^[6]；CHB的临床诊断符合《慢性乙型肝炎防治指南(2015年版)》^[7]。排除标准为合并其他病毒性肝炎或由酒精、药物等原因导致的肝脏疾病；肝癌或其他恶性肿瘤伴有肝脏转移；患有自身免疫性疾病、HIV感染、妊娠或有严重的心、肺、肾脏、神经系统等疾病。

1.2 标本采集与储存

采用EDTA-K2抗凝的真空采血管于清晨空腹采集外周静脉血约3 mL，取100 μL用于流式细胞仪检测外周血MDSC频数，剩余全血分离外周血单个核细胞(peripheral blood mononuclear cells, PBMC)。所有标本均为新鲜抗凝血，且在采集后4 h内处理。

1.3 流式细胞仪检测外周血免疫细胞的水平

MDSC细胞检测：取100 μL全血加至流式管中，然后分别加入CD11b-APC、CD33-PE和HLA-DR-PE/Cy7流式抗体各2 μL，避光孵育15 min后，每管加入溶血素500 μL，室温避光静置15 min，离心洗涤，加入流式鞘液300 μL重悬细胞，流式细胞仪检测MDSC细胞比例。

Treg细胞检测：收集PBMC，加入CD4-FITC和CD25-APC抗体各2 μL，4 ℃避光孵育30 min。经固定破膜处理后，离心洗涤2次，重悬细胞，然后加入10 μL Foxp3-PE抗体，4 ℃避光孵育45 min，离心洗涤，加入流式鞘液200 μL重悬细胞，流式细胞仪检测。

Th17、Tc17细胞检测：收集PBMC，用佛波醇酯和离子霉素刺激，同时加入GolgiStop，4 h后收集细胞至流式管，加入CD4-FITC和CD8a-PerCP/Cy5.5抗体各2 μL，4 ℃避光孵育30 min。经固定破膜处理后，离心洗涤2次，然后加入10 μL IL-17-APC抗体，避光孵育30 min，离心洗涤，加入流式鞘液200 μL重悬细胞，流式细胞仪检测。

1.4 临床检验指标的测定

肝功能检测采用美国雅培公司C8000型生化分析仪，HBeAg检测采用瑞士罗氏公司全自动Cobase601型电化学发光免疫分析仪，血常规检测采用日本希森美康公司XN1000血液分析仪，并计算炎症指标，包括中性粒细胞/淋巴细胞比值(neutrophil-to-lymphocyte ratio, NLR)、单核细胞/淋巴细胞比值(monocyte-to-lymphocyte ratio, MLR)、血小板/淋巴细胞比值(platelet-to-lymphocyte ratio, PLR)与全身免疫炎症指数(systemic immune inflammation index，SIRS)，SIRS=中性粒细胞×血小板/淋巴细胞。

1.5 统计学方法

采用SPSS 21.0统计软件进行数据分析。符合正态分布的计量资料以x±s表示，多组间比较采用独立样本方差分析，组间进一步两两比较采用LSD-t检验；不服从正态分布的计量资料以M(P₂₅~P₇₅)表示，多组间比较采用Kruskal-Wallis H检验，进一步两两比较采用Nemenyi检验。变量间的相关性采用Pearson线性相关或Spearman秩相关分析，P＜0.05为差异有统计学意义。

2. 结果

2.1 一般资料

四组间年龄与性别构成具有可比性，差异均无统计学意义(P值均＞0.05)。ACHBLF组、pre-ACHBLF组和CHB组患者ALT与AST水平、HBeAg阳性比均明显高于HC组(P值均＜0.05)。ACHBLF组和pre-ACHBLF组患者TBil水平均明显高于HC组(P值均＜0.05)。ACHBLF组和pre-ACHBLF组患者PTA水平均明显低于CHB组(P值均＜0.05)(表 1)。

表 1 四组研究对象一般特征比较

Table 1. Comparison of general characteristics of four groups

组别	例数	年龄(岁)	性别(男/女，例)	ALT(U/L)	AST(U/L)	HBeAg (+/-)	TBil(μmol/L)	PTA(%)
ACHBLF组	15	45.00±10.86	11/4	67.00(33.00~132.00)¹⁾	88.00(57.70~117.00)¹⁾	10/5¹⁾	279.00(241.00~337.00)¹⁾	38.00(25.00~65.50)²⁾
pre-ACHBLF组	15	48.50±7.47	11/4	57.40(31.20~114.00)¹⁾	54.90(34.00~112.00)¹⁾	10/5¹⁾	76.40(54.70~129.00)¹⁾	43.90(37.50~51.00)²⁾
CHB组	15	43.10±11.72	12/3	57.00(44.00~261.00)¹⁾	46.00(29.00~135.00)¹⁾	12/3¹⁾	20.00(12.00~33.00)¹⁾	84.00(70.50~91.20)
HC组	15	42.10±6.37	12/3	21.10(11.00~32.30)	21.70(17.20~27.90)	0/15	11.60(9.90~14.40)	-
统计值		F=1.326	χ²=0.373	F=18.594	F=27.525	χ²=23.571	F=48.781	F=26.217
P值		0.275	0.946	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001
注：与HC组比较，1)P＜0.05；与CHB组比较，2)P＜0.05。

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2.2 MDSC、Th17、Treg和Tc17细胞的表达

MDSC、Th17、Treg和Tc17细胞水平在四组间差异均有统计学意义(P值均＜0.01)。与CHB组比较，ACHBLF和pre- ACHBLF组的Th17、Treg和Tc17细胞水平均明显升高(P值均＜0.05)，同时pre-ACHBLF患者的MDSC细胞水平明显升高，差异有统计学意义(P＜0.05)(图 1)。

图 1 四组研究对象MDSC、Th17、Treg和Tc17表达水平

Figure 1. The expression levels of MDSC, Th17, Treg and TC17 in four groups

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2.3 pre-ACHBLF患者中免疫细胞与炎症指标的关系

相关性分析显示，在pre-ACHBLF患者中，MDSC与白细胞数、中性粒细胞数及NLR、MLR、SIRS呈正相关(P值均＜0.05)，而Treg细胞仅与白细胞数呈正相关(P=0.043)。相反，Th17/Treg值和Tc17细胞水平与淋巴细胞数呈负相关(P值均＜0.05)(表 2)。

表 2 pre-ACHBLF患者免疫细胞水平与炎症指标的关系

Table 2. The relationship between the expression of immune cells and the degree of inflammation in patients with pre-ACHBLF

炎症指标	MDSC		Th17		Treg		Th17/Treg		Tc17
炎症指标	r值	P值	r值	P值	r值	P值	r值	P值	r值	P值
白细胞	0.775	0.001	0.167	0.668	0.618	0.043	-0.286	0.493	-0.533	0.139
中性粒细胞	0.727	0.002	0.250	0.516	0.491	0.125	-0.238	0.570	-0.417	0.265
淋巴细胞	0.079	0.781	-0.611	0.081	0.333	0.318	-0.790	0.020	-0.795	0.010
NLR	0.571	0.026	0.483	0.187	0.255	0.450	0.048	0.911	-0.150	0.700
MLR	0.786	0.001	0.233	0.546	0.236	0.484	0.024	0.955	-0.433	0.244
PLR	0.161	0.567	0.600	0.088	0.018	0.958	0.429	0.289	0.106	0.787
SIRS	0.846	＜0.001	0.233	0.546	0.336	0.312	0.024	0.955	-0.433	0.244

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3. 讨论

ACHBLF的发生发展涉及固有免疫和获得性免疫的多个环节，已有许多研究^[8]证实免疫细胞如树突状细胞、巨噬细胞、MDSC、T淋巴细胞等与ACHBLF的进展及预后密切相关，但尚未有研究评价这些细胞在pre-ACHBLF中的变化。本研究发现pre-ACHBLF患者中MDSC、Th17、Treg和Tc17均比CHB患者明显升高，提示ACHBLF前期已存在严重的细胞免疫功能紊乱。因此，关注患者的病期变化，对pre-ACHBLF患者及早进行干预，维持细胞免疫平衡，可能对防止疾病进展起到重要作用。

MDSC是机体重要的免疫抑制细胞，具有抑制固有免疫和适应性免疫应答的作用，参与多种炎症和免疫性疾病的发生发展。近年来，一些研究^[9]相继分析了MDSC在ACHBLF中的作用，发现ACHBLF患者MDSC频数明显升高，且随患者病期程度加重，本研究也证实了以上结果。为了探讨MDSC在ACHBLF疾病进程中的变化，本研究还观察了pre-ACHBLF患者中MDSC的频数，结果发现pre-ACHBLF患者中MDSC频数明显高于CHB患者，且高于ACHBLF患者。推测随着各种诱因导致CHB患者病情加重，肝细胞坏死及炎症反应产生的多种因子诱导机体MDSC快速升高，以抑制过强的免疫反应；而在ACHBLF发生时，机体以免疫炎症反应占主导，使MDSC频数相应降低。目前国内外关于MDSC在CHB-pre-ACHBLF-ACHBLF进程中如何发挥作用的研究还很少，尚需要进一步深入探讨。

值得注意的是，笔者团队观察到MDSC和Treg细胞的变化过程存在差异，MDSC水平在pre-ACHBLF患者中最高，而Treg细胞在ACHBLF患者中更高，此结果提示MDSC的增殖具有相对前置性，这两类重要的抑制性细胞在ACHBLF进展的不同时期分别发挥作用。此外还发现pre-ACHBLF患者中MDSC水平与炎症指标之间存在正相关性，而Treg水平仅与白细胞数呈正相关。目前炎症和免疫反应是ACHBLF发生发展过程中的重要病理生理机制，随着系统性炎症反应综合征的发生，炎症介质的持续释放导致ACHBLF患者出现血管内皮障碍，毛细血管渗漏和组织灌注不足而引起微循环紊乱, 继而引发器官衰竭。既往研究表明循环炎症介质及肝细胞坏死释放的损伤相关模式分子等可促进MDSC的增殖和分化^[10]，而MDSC具有诱导Treg细胞扩增和分化的作用^[11-12]。因此，这些研究提示在肝衰竭前期，过强的炎症反应激活MDSC使其表达升高，形成以MDSC为主的免疫抑制状态；随着疾病进展，MDSC诱导Treg细胞表达，转变为以Treg细胞为主的状态。

Th17和Tc17是以分泌IL-17为主要特征的T淋巴细胞亚群，他们有着共同的分化刺激因子及分化调节通路，共同在炎症性疾病、病毒感染、自身免疫性疾病、肿瘤等疾病中发挥作用^[13]。但是，这两类细胞在许多方面如分子和代谢调节网络、可塑性、表达变化也存在差异^[14]。一项肝癌相关的研究^[15]显示，肝癌组织中分泌IL-17的细胞主要是Tc17细胞，而外周血中则主要是Th17细胞。在ACHBLF病理损伤过程中，已有大量研究证实ACHBLF患者的Th17细胞明显升高，且与预后密切相关^[16]，但仅有极少数研究报道了Tc17细胞在ACHBLF发生发展中的作用^[17-18], 特别是罕有研究观察Tc17细胞在pre-ACHBLF患者的表达。本研究发现pre-ACHBLF和ACHBLF患者外周血Th17与Tc17细胞频数显著高于CHB患者，且Tc17的变化更明显，推测Th17与Tc17细胞可能协同参与ACHBLF的疾病进程。

综上所述，本研究发现pre-ACHBLF患者外周血MDSC、Treg、Th17细胞和Tc17细胞水平均明显升高，特别是MDSC表达与炎症指标呈密切相关性。尽管本研究病例数偏少，但研究结果对理解ACHBLF的疾病进展具有重要意义。将来进一步扩大样本量进行验证，并深入探讨这些免疫细胞的作用机制，将为ACHBLF早期抗免疫治疗提供更多的理论依据。

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沈阳化工大学材料科学与工程学院沈阳 110142

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Establishment of a predictive model of death within 30 days for patients with liver cirrhosis and bacterial ascites

DOI: 10.3969/j.issn.1001-5256.2019.11.017