吲哚菁绿清除试验联合总胆红素留存率对人工肝治疗HBV相关慢加急性肝衰竭患者短期预后的评估价值

都泓莲; 李烨; 王波; 马琳坤; 胡甜甜; 盛云建; 陈文; 吴刚; 邓存良

doi:10.3969/j.issn.1001-5256.2023.02.009

吲哚菁绿清除试验联合总胆红素留存率对人工肝治疗HBV相关慢加急性肝衰竭患者短期预后的评估价值

DOI: 10.3969/j.issn.1001-5256.2023.02.009

西南医科大学附属医院感染科，四川泸州 646000

基金项目:

四川省卫生健康委员会医学科技项目 (21PJ098)

伦理学声明：本研究方案于2022年1月7日经由西南医科大学附属医院伦理委员会审批，批号：KY2022003。

利益冲突声明：本研究不存在研究者、伦理委员会成员、受试者监护人以及与公开研究成果有关的利益冲突。

作者贡献声明：都泓莲、李烨、盛云建负责课题设计，资料分析，撰写论文；都泓莲、王波、马琳坤、胡甜甜参与收集数据，修改论文；陈文、吴刚、邓存良负责拟定写作思路，指导撰写文章。

详细信息

通信作者:
邓存良，dengcunl64@vip.sina.com (ORCID: 0000-0002-9120-8661)

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出版历程
- 收稿日期: 2022-07-19
- 录用日期: 2022-08-25
- 出版日期: 2023-02-20

Value of indocyanine green clearance test combined with total bilirubin actual resident rate in evaluating the short-term prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure after artificial liver support system therapy

Department of Infectious Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China

Research funding:

Medical Science and Technology Project of the Health Commission of Sichuan Province (21PJ098)

More Information

Corresponding author: DENG Cunliang, dengcunl64@vip.sina.com (ORCID: 0000-0002-9120-8661)

摘要

摘要: 目的联合吲哚菁绿清除试验(ICG)和总胆红素留存率(TBARR)建立预测经人工肝治疗的HBV相关慢加急性肝衰竭(HBV-ACLF)患者短期预后的新模型。方法回顾性收集2017年6月—2021年7月西南医科大学附属医院感染科收治的136例经人工肝治疗的HBV-ACLF患者的临床资料，据随访3个月时的转归情况分为存活组(n=92)和死亡组(n=44)，检测确诊ACLF时的生化、凝血、吲哚菁绿15分钟滞留率(ICG R₁₅)及肝有效血流量(EHBF)等指标，计算终末期肝病模型(MELD)评分、MELD差值(ΔMELD)、Child-Turcotte-Pugh(CTP)评分、总胆红素清除率(TBCR)、总胆红素反弹率(TBRR)和TBARR。偏态分布的计量资料两组间比较采用Mann-Whitney U检验；计数资料两组间比较则采用χ²检验。应用二分类Logistic回归分析法，构建人工肝治疗HBV-ACLF短期预后的联合预测模型。应用受试者工作特征曲线下面积(AUC)评估各种模型对人工肝治疗HBV-ACLF短期预后判断的准确性，AUC的比较采用Z检验。结果死亡组与存活组两组间比较，MELD评分、ΔMELD、CTP评分、ICG R₁₅、EHBF、TBRR、TBARR、中性粒细胞计数、中性粒细胞百分比、淋巴细胞计数、PLT、ALP、GGT、Alb、PT、INR、PTA、总胆红素、前白蛋白、纤维蛋白原、血清Na、年龄及肝性脑病发生率差异均有统计学意义(P值均＜0.05)。多因素Logistic回归分析显示，年龄(OR=1.096，95%CI：1.056~1.137，P＜0.001)、中性粒细胞计数(OR=1.214，95%CI：1.044~1.411，P=0.012)、TBRR(OR=0.989，95%CI：0.982~0.996，P=0.001)、TBARR(OR=1.073，95%CI：1.049~1.098，P＜0.001)、ΔMELD(OR=1.480，95%CI：1.288~1.701，P＜0.001)、CTP评分(OR=2.081，95%CI：1.585~2.732，P＜0.001)以及ICG R₁₅(OR=1.116，95%CI：1.067~1.168，P＜0.001)是经人工肝治疗HBV-ACLF患者短期死亡的独立影响因素。应用二分类Logistic回归分析，构建了4种人工肝治疗HBV-ACLF短期预后的联合预测模型，分别为TBRR-ICG R₁₅、TBARR-ICG R₁₅、TBARR-ICG R₁₅-ΔMELD和TBARR-ICG R₁₅-ΔMELD-年龄，AUC分别为0.830、0.867、0.900、0.917，联合预测模型的AUC高于单一指标(年龄、中性粒细胞计数、TBRR、TBARR、ΔMELD、MELD评分、CTP评分、ICG R₁₅)，其中TBARR-ICG R₁₅-ΔMELD-年龄模型的AUC最大。联合模型TBARR-ICG R₁₅-ΔMELD和TBARR-ICG R₁₅-ΔMELD-年龄的敏感度和特异度均在80%以上。结论 ICG R₁₅联合TBARR构建的联合预测模型对人工肝治疗HBV-ACLF患者的短期预后有较好的预测价值，联合模型对预后判断的准确性优于单一模型。
- 乙型肝炎病毒 /
- 慢加急性肝功能衰竭 /
- 肝，人工 /
- 预后 /
- 吲哚菁绿清除试验 /
- 总胆红素留存率
Abstract: Objective To establish a new model of indocyanine green (ICG) clearance test combined with total bilirubin actual resident rate (TBARR) for predicting the short-term prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) treated with artificial liver support system (ALSS) therapy. Methods A retrospective analysis was performed for the clinical data of 136 patients with HBV-ACLF who underwent ALSS therapy in Department of Infectious Diseases, The Affiliated Hospital of Southwest Medical University, from June 2017 to July 2021, and according to the prognosis at 3-month follow-up, they were divided into survival group with 92 patients and death group with 44 patients. Related indicators were measured at the time of the confirmed diagnosis of ACLF, including biochemical parameters, coagulation, indocyanine green retention rate at 15 minutes (ICGR₁₅), and effective hepatic blood flow (EHBF), and related indices were calculated, including Model for End-Stage Liver Disease (MELD) score, MELD difference (ΔMELD), Child-Turcotte-Pugh (CTP) score, total bilirubin clearance rate (TBCR), total bilirubin rebound rate (TBRR), and TBARR. The Mann-Whitney U test was used for comparison of continuous data with skewed distribution between two groups; the chi-square test was used for comparison of categorical data between groups. A binary logistic regression analysis was used to establish a combined predictive model for the prognosis of HBV-ACLF after ALSS therapy. The area under the ROC curve (AUC) was used to compare the accuracy of various models in judging the short-term prognosis of patients with HBV-ACLF after ALSS therapy, and the Z test was used for comparison of AUC. Results There were significant differences between the death group and the survival group in MELD score, ΔMELD, CTP score, ICGR₁₅, EHBF, TBRR, TBARR, neutrophil count, percentage of neutrophils, lymphocyte count, platelet count, alkaline phosphatase, gamma-glutamyl transpeptidase, total bilirubin, albumin, prothrombin time, international normalized ratio, prothrombin time activity, prealbumin, fibrinogen, serum sodium, age, and the incidence rate of hepatic encephalopathy (all P < 0.05). The multivariate logistic regression analysis showed that age (odds ratio [OR]=1.096, 95% confidence interval [CI]: 1.056-1.137, P < 0.001), neutrophil count (OR=1.214, 95%CI: 1.044-1.411, P=0.012), TBRR (OR=0.989, 95%CI: 0.982-0.996, P=0.001), TBARR (OR=1.073, 95%CI: 1.049-1.098, P < 0.001), ΔMELD (OR=1.480, 95%CI: 1.288-1.701, P < 0.001), CTP score (OR=2.081, 95%CI: 1.585-2.732, P < 0.001), and ICGR₁₅(OR=1.116, 95%CI: 1.067-1.168, P < 0.001) were independent influencing factors for short-term mortality in patients with HBV-ACLF after ALSS therapy. The binary logistic regression analysis was used to establish four combined predictive models for predicting the prognosis of HBV-ACLF after ALSS therapy, i.e., TBRR-ICGR₁₅, TBARR-ICGR₁₅, TBARR-ICGR₁₅-ΔMELD, and TBARR-ICGR₁₅-ΔMELD-age, with an AUC of 0.830, 0.867, 0.900, and 0.917, respectively, and the combined predictive models had a larger AUC than each index alone (age, neutrophil count, TBRR, TBARR, ΔMELD, MELD score, CTP score, and ICGR₁₅), among which the TBARR-ICGR₁₅-ΔMELD-age model had the largest AUC. The combined models TBARR-ICGR₁₅-ΔMELD and TBARR-ICGR₁₅-ΔMELD-age had sensitivities and specificities of > 80%. Conclusion The combined predictive model established by ICGR₁₅ and TBARR has a good value for in predicting the short-term prognosis of patients with HBV-ACLF after ALSS therapy, and the combined predictive model has a better accuracy than the single model in judging prognosis.
- Hepatitis B Virus /
- Acute-On-Chronic Liver Failure /
- Liver, Artificial /
- Prognosis /
- Indocyanine Green Clearance Test /
- Total Bilirubin Actual Resident Rate

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图 1 部分单项指标和联合预测模型的ROC曲线

Figure 1. ROC curves of partial single indicators and combined predictive models

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表 1 存活组和死亡组各项指标比较

Table 1. Comparison of various indicators between survival and non-survival group

项目	总数(n=136)	存活组(n=92)	死亡组(n=44)	统计值	P值
男/女(例)	117/19	80/12	37/7	χ²=0.203	0.652
年龄(岁)	50(42~56)	47(38~54)	54(45~60)	Z=-5.625	＜0.001
肝硬化[例(%)]	70(51.47)	42(45.65)	28(63.64)	χ²=3.854	0.050
HBeAg阳性[例(%)]	48(35.29)	34(36.96)	14(31.82)	χ²=0.344	0.557
HBV DNA载量(log₁₀)	6.62(4.80~7.39)	6.59(4.74~7.38)	6.66(4.95~7.41)	Z=-0.293	0.770
腹膜炎[例(%)]	115(84.56)	75(81.52)	40(90.91)	χ²=1.354	0.245
肝性脑病[例(%)]	30(22.06)	7(7.61)	23(52.27)	χ²=34.536	＜0.001
人工肝次数	3(3~4)	3(3~4)	3(3~5)	Z=-1.913	0.056
WBC(×10⁹/L)	6.88(5.40~8.56)	6.81(5.24~8.56)	7.05(5.51~8.27)	Z=-1.215	0.224
中性粒细胞百分比(%)	73.60(67.70~79.60)	71.65(65.95~75.68)	78.50(72.85~82.80)	Z=-6.721	＜0.001
中性粒细胞计数(×10⁹/L)	4.97(3.71~6.40)	4.79(3.44~6.40)	5.16(4.14~6.81)	Z=-2.743	0.006
淋巴细胞计数(×10⁹/L)	1.02(0.79~1.42)	1.10(0.85~1.51)	0.84(0.63~1.15)	Z=-6.639	＜0.001
Hb(g/L)	133(120~148)	134(122~148)	132(120~147)	Z=-0.729	0.466
PLT(×10⁹/L)	108(81~138)	115(92~148)	91(71~115)	Z=-5.713	＜0.001
ALT(U/L)	848.7(308.0~1 513.8)	924.1(303.2~1 580.4)	648.3(345.9~1 346.0)	Z=-1.723	0.085
AST(U/L)	558.3(234.6~1 298.5)	598.3(235.9~1 304.4)	557.5(222.05~1 170.6)	Z=-0.207	0.836
ALP(U/L)	154.0(123.6~187.2)	146.0(118.3~187.2)	164.8(133.5~190.8)	Z=-1.970	0.049
GGT(U/L)	116.5(70.9~176.3)	118.9(80.1~188.5)	104.8(64.3~160.3)	Z=-2.615	0.009
pAlb(g/L)	44.4(29.6~62.9)	45.8(31.6~63.4)	41.2(21.6~61.2)	Z=-2.400	0.016
Alb(g/L)	32.3(29.0~35.2)	32.6(29.0~36.3)	31.0(28.5~33.8)	Z=-2.819	0.005
TBil(μmol/L)	300.3(226.3~381.1)	286.1(194.7~366.1)	341.9(252.3~461.0)	Z=-4.424	＜0.001
PT(s)	23.4(20.6~26.6)	21.7(19.8~25.6)	25.5(23.9~31.8)	Z=-9.192	＜0.001
INR	2.09(1.78~2.42)	1.90(1.69~2.26)	2.29(2.12~2.99)	Z=-9.425	＜0.001
PTA(%)	37(32~45)	41(35~47)	34(25~37)	Z=-9.312	＜0.001
Fib(g/L)	1.67(1.43~1.92)	1.74(1.52~2.03)	1.57(1.39~1.82)	Z=-4.286	＜0.001
肌酐(μmol/L)	65.6(58.2~75.8)	66.3(57.8~75.2)	65.6(58.2~76.9)	Z=-0.139	0.889
K(mmol/L)	3.94(3.64~4.30)	3.93(3.65~4.27)	4.04(3.61~4.41)	Z=-0.670	0.503
Na(mmol/L)	137.4(135.6~139.6)	138.0(135.8~139.7)	136.6(134.2~139.3)	Z=-2.369	0.018
AFP(ng/mL)	47.37(15.05~138.32)	59.14(15.05~169.96)	40.00(14.46~123.5)	Z=-1.587	0.112
TBCR(%)	48.86(31.99~61.17)	46.45(33.09~61.74)	52.67(31.19~60.31)	Z=-0.646	0.518
TBRR(%)	63.19(26.21~113.26)	57.07(19.43~99.95)	72.64(42.22~149.55)	Z=-4.335	＜0.001
TBARR(%)	87.32(71.92~97.40)	78.54(70.65~90.96)	96.12(88.77~102.78)	Z=-8.587	＜0.001
MELD评分	22.19(19.93~25.22)	21.46(18.32~23.62)	24.62(21.92~28.22)	Z=-7.495	＜0.001
△MELD(分)	5.62(3.48~8.15)	4.64(3.04~6.74)	8.00(5.36~10.05)	Z=-7.736	＜0.001
CTP评分	11(10~12)	10(9~11)	12(11~13)	Z=-9.977	＜0.001
ICG R₁₅(%)	52.5(45.8~58.3)	50.9(42.5~54.9)	56.6(50.9~63.8)	Z=-8.236	＜0.001
EHBF(L/min)	0.18(0.14~0.22)	0.20(0.16~0.25)	0.15(0.12~0.18)	Z=-8.276	＜0.001
注：pAlb，前白蛋白；Fib，纤维蛋白原。

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表 2 单项指标和联合预测模型的AUC、敏感度、特异度及临界值

Table 2. AUC, sensitivity, specificity and cut-off values of single indicators and combined predictive models

项目	AUC	敏感度(%)	特异度(%)	临界值
年龄(岁)	0.667	70.45	55.43	49
中性粒细胞计数(×10⁹/L)	0.601	47.73	69.57	5.57
TBRR(%)	0.671	52.27	76.09	94.36
TBARR(%)	0.788	81.82	69.57	85.46
△MELD(分)	0.760	63.64	79.35	7.03
MELD评分	0.751	65.91	78.26	23.75
CTP评分	0.781	93.18	51.09	10
ICG R₁₅(%)	0.773	50.00	89.13	58.3
TBRR-ICG R₁₅	0.830	61.36	90.22	-0.1
TBARR-ICG R₁₅	0.867	84.09	79.35	-0.8
TBARR-ICG R₁₅-△MELD	0.900	86.36	80.43	-0.76
TBARR-ICG R₁₅-△MELD-年龄	0.917	88.64	85.87	-0.6

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