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中华消化病与影像杂志(电子版) ›› 2024, Vol. 14 ›› Issue (02) : 114 -120. doi: 10.3877/cma.j.issn.2095-2015.2024.02.003

论著

基于列线图模型对慢性乙型肝炎合并肝脏脂肪变性患者并发晚期肝纤维化的临床预测
江浩1, 余宏圣1, 杨碧兰1, 阿布都克尤木·斯马依1, 吴斌1, 杨逸冬1,()   
  1. 1. 510630 广州,中山大学附属第三医院消化内科;510630 广州,广东省肝病重点实验室
  • 收稿日期:2023-09-08 出版日期:2024-04-01
  • 通信作者: 杨逸冬
  • 基金资助:
    广东省自然科学基金杰出青年项目(2022B1515020024)

Nomogram model-based clinical prediction of advanced hepatic fibrosis in patients with chronic hepatitis B complicated by hepatic steatosis

Hao Jiang1, Hongsheng Yu1, Bilan Yang1, Smayi Abdukyamu1, Bin Wu1, Yidong Yang1,()   

  1. 1. Department of Gastroenterology, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China; Guangdong Provincial Key Laboratory of Liver Disease Research, Guangzhou 510630, China
  • Received:2023-09-08 Published:2024-04-01
  • Corresponding author: Yidong Yang
引用本文:

江浩, 余宏圣, 杨碧兰, 阿布都克尤木·斯马依, 吴斌, 杨逸冬. 基于列线图模型对慢性乙型肝炎合并肝脏脂肪变性患者并发晚期肝纤维化的临床预测[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(02): 114-120.

Hao Jiang, Hongsheng Yu, Bilan Yang, Smayi Abdukyamu, Bin Wu, Yidong Yang. Nomogram model-based clinical prediction of advanced hepatic fibrosis in patients with chronic hepatitis B complicated by hepatic steatosis[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2024, 14(02): 114-120.

目的

探讨慢性乙型肝炎(CHB)合并肝脏脂肪变性患者并发晚期肝纤维化(S3~S4)的独立危险因素,构建及验证列线图风险预测模型。

方法

选取2008年8月至2020年12月在中山大学附属第三医院就诊并行经皮肝脏穿刺活检的439例未经抗病毒治疗的CHB合并肝脏脂肪变性患者作为研究对象,按2∶1随机分为训练组293例和验证组146例。采用logistic回归筛选出晚期纤维化的相关危险因素并构建列线图风险预测模型。

结果

logistic回归分析显示血小板(OR=0.987,P=0.003)、凝血酶原时间(OR=1.952,P=0.011)、球蛋白(OR=1.260,P=0.001)是晚期纤维化的独立危险因素。列线图风险预测模型在训练组中预测晚期纤维化的曲线下面积为0.866,显著优于天冬氨酸转氨酶和血小板比率指数(0.782,P=0.017)、γ-谷氨酰转肽酶/血小板比值(0.753,P=0.004)、肝纤维化4因子指数(0.780,P=0.024)、非酒精性脂肪肝纤维化评分(0.737,P<0.001)、BARD评分(0.595,P<0.005)模型;在验证组中也得到了类似的结果(P<0.05)。校准曲线和决策曲线显示列线图模型具有较高的一致性和临床净收益。

结论

本研究基于CHB合并肝脏脂肪变性的晚期肝纤维化患者的独立危险因素构建了列线图风险预测模型,经验证,该模型具有较高的预测效能、一致性和临床净收益。

Objective

To identify the independent risk factors associated with advanced fibrosis (S3-S4) in patients suffering from chronic hepatitis B (CHB) complicated with hepatic steatosis, and to develop and validate a nomogram risk prediction model.

Methods

A total of 439 treatment-naïve CHB patients who had hepatic steatosis and underwent liver biopsy in the Third Affiliated Hospital of Sun Yat-sen University between August 2008 and December 2020 were recruited as research objects. These patients were then randomly allocated at a 2∶1 ratio into the training set (n=293) and the validation set (n=146). Logistic regression was used to identify the risk factors of advanced fibrosis. A nomogram prediction model was subsequently created.

Results

Logistic regression analysis revealed that platelet (OR=0.987, P=0.003), prothrombin time (OR=1.952, P=0.011), and globulin (OR=1.260, P=0.001) were the independent risk factors for advanced fibrosis. The area under the receiver operating characteristic (ROC) curve for the proposed nomogram model in the training group, which predicted advanced fibrosis, was noted to be 0.866. This was considerably higher than the aspartate aminotransferase-to-platelet ratio index (0.782, P=0.017), gamma-glutamyl transpeptidase-to-platelet ratio (0.753, P=0.004), fibrosis-4 score (0.780, P=0.024), non-alcoholic fatty liver disease fibrosis score (0.737, P<0.001), and BARD score (0.595, P<0.001) models; similar findings were observed in the validation set (P<0.05). Calibration curve and decision curve demonstrated that the nomogram model had high consistency and clinical net benefit.

Conclusion

A nomogram risk prediction model based on independent risk factors of advanced liver fibrosis patients with CHB combined with hepatic steatosis is constructed in this study. After verification, the model has high predictive efficiency, consistency, and clinical net gain.

表1 训练组与验证组患者基线资料比较
基线资料 训练组(n=293) 验证组(n=146) 统计量值 P
年龄[岁,M(Q1,Q3)] 36.00(31.00,42.00) 36.00(30.75,43.00) Z=0.272 0.786
男性[例(%)] 255(87.0) 129(88.4) c2=0.156 0.693
体重指数(kg/m2,±s) 24.88±3.50 24.35±3.20 t=-1.522 0.129
血小板(×10-9/L,±s) 210.40±54.71 220.9±52.59 t=-1.370 0.171
天门冬氨酸氨基转移酶[U/L,M(Q1,Q3)] 38.00(28.00,58.00) 42.00(31.00,65.25) Z=-1.646 0.100
丙氨酸氨基转移酶[U/L,M(Q1,Q3)] 29.00(24.00,37.00) 31.00(24.00,41.50) Z=-1.371 0.171
γ-谷氨酰转移酶[U/L,M(Q1,Q3)] 31.00(22.00,45.00) 34.00(24.00,50.25) Z=-1.360 0.174
白蛋白[g/L,M(Q1,Q3)] 45.80(43.80,47.80) 45.55(43.28,47.30) Z=1.502 0.133
球蛋白(g/L,±s) 26.85±4.20 27.79±4.45 t=1.927 0.052
甲胎蛋白[μg/mL,M(Q1,Q3)] 3.02(2.59,4.59) 3.38(2.10,5.25) Z=-1.317 0.188
总胆固醇(mmol/L,±s) 4.93±0.91 4.98±0.89 t=0.525 0.600
甘油三酯[mmol/L,M(Q1,Q3)] 1.28(0.94,1.92) 1.26(0.94,1.93) Z=0.259 0.796
高密度脂蛋白胆固醇[mmol/L,M(Q1,Q3)] 1.13(0.97,1.28) 1.12(0.98,1.30) Z=-0.395 0.693
低密度脂蛋白胆固醇(mmol/L,±s) 3.24±0.81 3.28±0.81 t=0.510 0.610
凝血酶原时间[s,M(Q1,Q3)] 13.20(12.70,13.70) 13.20(12.80,13.83) Z=-1.106 0.629
2型糖尿病[例(%)] 14(4.8) 9(6.2) c2=0.377 0.539
糖耐量受损[例(%)] 12(4.1) 5(3.4) c2=0.118 0.731
高血压[例(%)] 41(14.0) 23(15.8) c2=0.242 0.622
乙型肝炎e抗原阳性[例(%)] 126(43.0) 61(41.8) c2=0.060 0.807
乙型肝炎DNA水平[Log10 IU/mL,M(Q1,Q3)] 4.79(3.43,6.68) 5.78(3.53,7.40) Z=-1.648 0.099
脂肪变性分级[例(%)]     c2=2.812 0.421
0 89(30.4) 49(33.6)    
1 177(60.4) 90(61.6)    
2 20(6.8) 5(3.4)    
3 7(2.4) 2(1.4)    
炎症分级[例(%)]     c2=7.910 0.095
0~1 135(46.0) 64(43.9)    
2 113(38.6) 47(32.2)    
3 34(11.6) 21(14.4)    
4 11(3.8) 14(9.6)    
纤维化分期[例(%)]     c2=9.016 0.061
0 38(13.0) 20(13.7)    
1 130(44.7) 44(30.0)    
2 71(24.2) 39(26.7)    
3 34(11.6) 27(18.5)    
4 20(6.8) 16(11.0)    
表2 训练组中与晚期纤维化相关的临床参数的单因素Logistic回归分析
表3 训练组中与晚期纤维化相关的临床参数的多因素Logistic回归分析
图1 预测慢性乙型肝炎合并肝脏脂肪变性患者并发晚期肝纤维化风险的列线图预测模型注:Points为单项得分;PLT为血小板;PT为凝血酶原时间;GLB为球蛋白;Total Points为总得分,以总得分向下划一条线所得的值即为诊断为晚期肝纤维化的概率(S3~S4)。
图2 各预测模型在训练组和验证组的受试者操作特征曲线注:APRI为天冬氨酸转氨酶和血小板比率指数;GPR为γ-谷氨酰转肽酶/血小板比值;FIB-4为肝纤维化4因子指数;NFS为非酒精性脂肪肝纤维化评分;BARD为bard评分;Nomogram为列线图模型;Sensitivity为灵敏度;Specificity为特异度;(2A)训练组,(2B)验证组。
表4 不同预测模型预测晚期纤维化的受试者操作特征曲线分析
图3 预测模型的校准曲线图注:Actually probability为实际风险概率;Predicted Pr为预测风险概率;Apparent为参考线;Bias-corrected为矫正后曲线;Ideal为理想曲线;(3A)训练组,(3B)验证组。
图4 决策曲线分析图注:Nomogram为列线图模型;APRI为天冬氨酸转氨酶和血小板比率指数;GPR为γ-谷氨酰转肽酶/血小板比值;FIB-4为肝纤维化4因子指数;NFS为非酒精性脂肪肝纤维化评分;BARD为bard评分;ALL为均发生晚期纤维化;None为均不发生晚期纤维化;Standardized Net Benefit为标准净收益;High Risk Threshold为风险阈值;Cost:Benefit Ratio为成本效益比;(4A)训练组,(4B)验证组。
表5 不同预测模型预测晚期纤维化的Hosmer-Lemeshow检验分析
[1]
Shi YW, Yang RX, Fan JG. Chronic hepatitis B infection with concomitant hepatic steatosis: Current evidence and opinion[J]. World J Gastroenterol, 2021, 27(26): 3971-3983.
[2]
Kleiner DE, Brunt EM, Van Natta M, et al. Nonalcoholic Steatohepatitis Clinical Research Network. Design and validation of a histological scoring system for nonalcoholic fatty liver disease[J]. Hepatology, 2005, 41(6): 1313-1321.
[3]
Wang MM, Wang GS, Shen F, et al. Hepatic steatosis is highly prevalent in hepatitis B patients and negatively associated with virological factors[J]. Dig Dis Sci, 2014, 59(10): 2571-2579.
[4]
Peleg N, Issachar A, Sneh Arbib O, et al. A. Liver steatosis is a strong predictor of mortality and cancer in chronic hepatitis B regardless of viral load[J]. JHEP Rep, 2019, 1(1): 9-16.
[5]
Scheuer PJ. Classification of chronic viral hepatitis: a need for reassessment[J]. J Hepatol, 1991, 13(3): 372-374.
[6]
Hanif H, Khan MM, Ali MJ, et al. A New Endemic of Concomitant Nonalcoholic Fatty Liver Disease and Chronic Hepatitis B[J]. Microorganisms, 2020, 8(10): 1526.
[7]
Li F, Ou Q, Lai Z, et al. The Co-occurrence of Chronic Hepatitis B and Fibrosis Is Associated With a Decrease in Hepatic Global DNA Methylation Levels in Patients With Non-alcoholic Fatty Liver Disease[J]. Front Genet, 2021, 12: 671552.
[8]
Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C[J]. Hepatology, 2003, 38(2): 518-526.
[9]
Thachil J. Relevance of clotting tests in liver disease[J]. Postgrad Med J, 2008, 84(990): 177-181.
[10]
Schmilovitz-Weiss H, Tovar A, et al. Predictive value of serum globulin levels for the extent of hepatic fibrosis in patients with chronic hepatitis B infection[J]. J Viral Hepat, 2006, 13(10): 671-677.
[11]
Buechler C, Wanninger J, Neumeier M. Adiponectin, a key adipokine in obesity related liver diseases[J]. World J Gastroenterol, 2011, 17(23): 2801-2811.
[12]
Feldstein AE, Canbay A, Angulo P, et al. Hepatocyte apoptosis and fas expression are prominent features of human nonalcoholic steatohepatitis[J]. Gastroenterology, 2003, 125(2): 437-443.
[13]
Zhang RN, Pan Q, Zhang Z, et al. Saturated Fatty Acid inhibits viral replication in chronic hepatitis B virus infection with nonalcoholic Fatty liver disease by toll-like receptor 4-mediated innate immune response[J]. Hepat Mon, 2015, 15(5): e27909.
[14]
European Association for Study of Liver; Asociacion Latinoamericana para el Estudio del Higado. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis[J]. J Hepatol, 2015, 63(1): 237-264.
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