基于LASSO回归的慢性乙型肝炎肝纤维化无创诊断模型的构建及验证

壮健; 朱韦文; 张超

doi:10.3969/j.issn.1001-5256.2022.08.014

基于LASSO回归的慢性乙型肝炎肝纤维化无创诊断模型的构建及验证

DOI: 10.3969/j.issn.1001-5256.2022.08.014

南京医科大学附属常州第二人民医院超声科，江苏常州 213000

基金项目:

江苏省卫生健康委2019年医学科研面上项目 (H201952)

伦理学声明：本研究于2019年8月17日经由南京医科大学附属常州第二人民医院伦理委员会审批通过，批号：2019-C013，并取得患者知情同意。

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：壮健负责课题设计，资料分析，撰写论文；张超参与收集数据，修改论文；朱韦文负责拟定写作思路，指导撰写文章并最后定稿。

详细信息

通信作者:
朱韦文，ww1234xiao@126.com

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出版历程
- 收稿日期: 2022-01-07
- 录用日期: 2022-03-16
- 出版日期: 2022-08-20

Establishment and validation of a noninvasive diagnostic model for chronic hepatitis B liver fibrosis based on LASSO regression

Department of Ultrasound, Changzhou Second People's Hospital, Nanjing Medical University, Changzhou, Jiangsu 213000, China

Research funding:

2019 General Medical Research Project of Jiangsu Provincial Health Commission (H201952)

More Information

Corresponding author: ZHU Weiwen, ww1234xiao@126.com(ORCID: 0000-0003-0159-2784)

摘要

摘要: 目的利用血清学指标建立基于LASSO回归的慢性乙型肝炎(CHB)肝纤维化的无创诊断模型，并评估该模型对CHB肝纤维化的诊断价值。方法纳入2019年9月—2021年9月南京医科大学附属常州第二人民医院诊断为CHB的240例患者为研究对象，根据肝穿刺活检和病理结果将其分为显著纤维化组175例(F2~4期)和无显著纤维化组65例(F0~1期)。比较两组患者性别、年龄、血生化指标和二维剪切波弹性成像测量肝脏硬度值(LSM)，根据LASSO回归和多因素logistic回归分析筛选肝纤维化的危险因素，建立列线图模型并绘制受试者工作特征曲线(ROC曲线)、Calibration曲线和Decision曲线进行验证。符合正态分布的计量资料多组间比较采用单因素方差分析，两两比较采用LSD-t检验；不符合正态分布的计量资料多组间比较采用Kruskal-Wallis H检验；计数资料多组间比较用χ²检验。结果 F3、F4期与F2、F0~1期患者年龄、ALT、AST、ALP、GGT、TBil、PLT、Ⅲ型前胶原、Ⅳ型胶原、透明质酸和LSM比较，差异均有统计学意义(P值均＜0.05)。采用LASSO回归筛选出5个重要变量，logistic回归分析显示，透明质酸(OR=1.432)、Ⅳ型胶原(OR=1.243)、Ⅲ型前胶原(OR=1.146)和LSM(OR=1.656)是肝纤维化的独立危险因素，而PLT(OR=0.567)是保护因素(P值均＜0.05)。F3和F4期患者列线图模型评分、LSM、APRI指数、King评分、Forns指数和FIB-4指数显著高于F2和F0~1期患者，差异均有统计学意义(P值均＜0.05)。ROC曲线分析列线图模型的预测价值，其AUC为0.876，显著高于LSM、APRI、King评分、Forns指数和FIB-4，差异均有统计学意义(P值均＜0.05)。Calibration曲线和Decision曲线显示列线图模型的一致性和获益性尚可。结论利用血清学指标透明质酸、Ⅳ型胶原、Ⅲ型前胶原、PLT和LSM，基于LASSO回归建立无创列线图模型作为临床诊断CHB肝纤维化的量化工具，具有较高的诊断效能，值得推广应用。
- 肝硬化 /
- 乙型肝炎, 慢性 /
- 诊断 /
- 模型, 统计学
Abstract: Objective To establish a noninvasive diagnostic model for chronic hepatitis B (CHB) liver fibrosis based on LASSO regression using serological parameters, and to investigate the value of this model in the diagnosis of CHB liver fibrosis. Methods A total of 240 patients who were diagnosed with CHB in Changzhou Second People's Hospital, Nanjing Medical University, from September 2019 to September 2021 were enrolled as subjects, and according to the results of liver biopsy and pathology, they were divided into significant liver fibrosis (stage F2-F4) group with 175 patients and non-significant liver fibrosis (stage F0-F1) group with 65 patients. The two groups were compared in terms of sex, age, blood biochemical parameters, and liver stiffness measurement (LSM) measured by two-dimensional shear wave elastography, and LASSO regression and the multivariate logistic regression analysis were used screen out the risk factors for liver fibrosis. A nomogram model was established and then verified by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups, and the least significant difference t-test was used for further comparison between two groups; the Kruskal-Wallis H 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. Results There were significant differences between the patients with stage F3/F4 liver fibrosis and those with stage F2/F0-F1 liver fibrosis in age, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, total bilirubin, platelet count, procollagen type Ⅲ, type Ⅳ collagen, hyaluronic acid, and LSM (all P < 0.05). Five important variables were screened out by LASSO regression, and the logistic regression analysis showed that hyaluronic acid (odds ratio [OR]=1.432, P < 0.05), type Ⅳ collagen (OR=1.243, P < 0.05), procollagen type Ⅲ(OR=1.146, P < 0.05), and LSM (OR=1.656, P < 0.05) were the independent risk factors for liver fibrosis, while platelet count (OR=0.567, P < 0.05) was a protective factor. Compared with the patients with stage F2/F0-F1 liver fibrosis, the patients with stage F3/F4 liver fibrosis had significantly higher score of the nomogram model, LSM, aspartate aminotransferase-to-platelet ratio index (APRI), King score, Forns index, and fibrosis-4 (FIB-4) index (all P < 0.05). The ROC curve was used to analyze the predictive value of the nomogram model, and the results showed an area under the ROC curve of 0.876, which was significantly higher than that of LSM, APRI, King score, Forns index, and FIB-4 (all P < 0.05). Calibration curve and decision curve showed that the nomogram model had acceptable consistency and benefit. Conclusion The noninvasive nomogram model based on LASSO regression is established by using serum parameters including hyaluronic acid, type Ⅳ collagen, procollagen type Ⅲ, platelet count, and LSM, and as a quantitative tool for the clinical diagnosis of CHB liver fibrosis, it has a high diagnostic efficiency and thus holds promise for clinical application.
- Liver Cirrhosis /
- Hepatitis B, Chronic /
- Diagnosis /
- Models, Statistical

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图 1 CHB肝纤维化的LASSO回归分析

Figure 1. Lasso regression analysis of HBV liver fibrosis

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图 2 CHB肝纤维化的列线图模型

Figure 2. Nomogram model of HBV liver fibrosis

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图 3 列线图模型与各评分系统预测CHB肝纤维化的ROC曲线

Figure 3. ROC curve of predicting liver fibrosis in HBV patients by nomogram model and different grading systems

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图 4 列线图模型的Calibration曲线

Figure 4. Calibration curve of nomograph model

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图 5 列线图模型的Decision曲线

Figure 5. Decision curve of nomogram model

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表 1 CHB肝纤维化不同分期患者的一般资料比较

Table 1. Comparison of baseline data in patients with chronic hepatitis B-related liver fibrosis at different stages

项目	F0~1期(n=65)	F2期(n=70)	F3期(n=65)	F4期(n=40)	统计值	P值
男/女(例)	35/30	37/33	36/29	22/18	χ²=0.031	0.860
年龄(岁)	56.3±7.8	56.6±7.7	59.6±9.2	61.2±9.6	F=4.956	0.013
抗病毒治疗[例(%)]	19(29.2)	23(32.9)	20(30.8)	15(37.5)	χ²=0.852	0.837
HBV DNA(×10³/mL)	3.5±1.6	3.7±1.8	3.6±1.5¹⁾²⁾	3.9±2.0¹⁾²⁾	F=1.124	0.169
ALT(U/L)	32.6(20.1~65.2)	34.2(21.2~68.9)	45.3(26.3~75.3)¹⁾²⁾	46.6(27.3~79.8)¹⁾²⁾	H=15.238	0.001
AST(U/L)	30.0(18.5~62.3)	32.3(19.6~64.5)	42.6(24.4~72.9)¹⁾²⁾	44.5(26.3~75.8)¹⁾²⁾	H=17.501	＜0.001
GGT(U/L)	52.3(30.2~70.2)	53.6(32.3~72.3)	65.9(42.3~75.9)¹⁾²⁾	67.8(44.2~76.9)¹⁾²⁾	H=11.384	0.007
Alb(g/L)	42.3(32.6~49.9)	42.0(32.1~50.1)	41.0(31.2~49.2)	41.3(31.3~52.6)	H=3.214	0.402
Glo(g/L)	28.9(22.3~35.6)	28.5(21.6~34.8)	27.4(21.0~34.9)	27.5(21.2~35.0)	H=1.987	0.741
ALP(U/L)	77.5(51.6~92.5)	79.2(53.3~93.6)	92.3(72.3~112.4)¹⁾²⁾	94.5(73.6~115.5)¹⁾²⁾	H=9.573	0.017
TBil(μmol/L)	10.2(8.2~15.5)	10.3(8.3~15.6)	15.6(11.1~18.9)¹⁾²⁾	16.2(11.3~20.1)¹⁾²⁾	H=8.465	0.028
DBil(μmol/L)	6.2(4.6~8.9)	6.3(4.5~9.0)	6.7(4.8~9.7)	6.6(4.5~10.2)	H=2.993	0.452
PLT(×10⁹/L)	175.9(132.0~301.2)	172.2(130.2~298.6)	105.6(92.3~164.5)¹⁾²⁾	100.2(90.1~156.8)¹⁾²⁾	H=18.247	＜0.001
总胆固醇(mmol/L)	5.0(4.0~5.7)	5.1(4.1~5.5)	5.3(4.2~5.9)	5.4(4.0~5.8)	H=2.342	0.620
低密度脂蛋白(mmol/L)	3.0(2.3~4.0)	3.1(2.3~4.2)	3.2(2.5~3.9)	3.1(2.3~3.8)	H=3.175	0.409
Ⅲ型前胶原(ng/mL)	5.8(4.2~8.3)	6.0(4.3~8.5)	9.2(6.0~12.3)¹⁾²⁾	9.4(6.3~15.2)¹⁾²⁾	H=16.519	＜0.001
Ⅳ型胶原(ng/mL)	42.5(28.9~62.3)	44.2(29.3~63.5)	59.8(43.6~70.5)¹⁾²⁾	62.4(44.5~73.6)¹⁾²⁾	H=22.356	＜0.001
透明质酸(ng/mL)	100.5(89.6~133.4)	102.3(86.5~136.5)	123.5(105.1~156.4)¹⁾²⁾	130.2(112.3~165.9)¹⁾²⁾	H=19.574	＜0.001
LSM(kPa)	10.0(8.2~15.6)	10.3(8.3~16.0)	14.5(11.0~18.9)¹⁾²⁾	15.2(11.3~20.1)¹⁾²⁾	H=14.268	0.002
注：与F0~1期比较，1)P＜0.05；与F2期比较，2)P＜0.05。

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表 2 CHB肝纤维化的多因素logistic回归分析

Table 2. Multivariate logistic regression analysis of HBV liver fibrosis

因素	β值	Wald	P值	OR	95%CI
透明质酸	0.801	9.629	＜0.001	1.432	1.212~1.895
Ⅳ型胶原	0.619	7.052	0.001	1.243	1.100~1.759
Ⅲ型前胶原	0.524	6.659	0.001	1.146	1.056~1.423
LSM	1.002	13.206	＜0.001	1.656	1.325~2.001
PLT	-0.346	5.624	0.007	0.567	0.232~0.865

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表 3 肝纤维化不同分期患者评分系统的比较

Table 3. Comparisons of different grading systems in patients with different stages of liver fibrosis

指标	F0~1期(n=65)	F2期(n=70)	F3期(n=65)	F4期(n=40)	H值	P值
列线图模型评分	0.58(0.33~0.86)	0.69(0.42~0.94)	1.02(0.68~1.43)¹⁾²⁾	1.56(1.12~1.89)¹⁾²⁾	17.352	＜0.001
LSM	10.0(8.2~15.6)	11.9(8.5~17.2)	14.0(10.0~17.8)¹⁾²⁾	16.3(12.3~18.9)¹⁾²⁾	9.517	0.017
APRI	0.29(0.13~0.37)	0.33(0.21~0.40)	0.59(0.32~0.72)¹⁾²⁾	0.67(0.39~0.83)¹⁾²⁾	11.241	0.007
King评分	7.05(5.26~8.23)	7.98(5.98~9.04)	12.23(6.59~15.54)¹⁾²⁾	13.64(7.89~17.26)¹⁾²⁾	15.431	＜0.001
Forns指数	7.23(5.64~8.24)	8.01(5.77~9.00)	9.65(7.26~13.25)¹⁾²⁾	10.52(8.01~15.59)¹⁾²⁾	11.587	0.006
FIB-4指数	1.23(1.00~1.67)	1.65(1.16~2.01)	2.48(1.56~3.12)¹⁾²⁾	2.79(1.79~3.55)¹⁾²⁾	9.368	0.018
注：与F0~1期比较，1)P＜0.05；与F2期比较，2)P＜0.05。

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表 4 列线图模型与各评分系统预测CHB肝纤维化的ROC曲线分析

Table 4. ROC curve of predicting liver fibrosis in HBV patients by nomogram model and different grading systems

指标	AUC	95%CI	P值	敏感度(%)	特异度(%)
列线图模型	0.876	0.806~0.923	＜0.001	82.3	86.9
LSM	0.800	0.823~0.856	0.002	72.0	76.3
APRI	0.745	0.701~0.824	0.005	66.5	70.4
King评分	0.698	0.612~0.752	0.008	61.3	64.9
Forns指数	0.644	0.602~0.723	0.010	60.2	62.2
FIB-4	0.601	0.564~0.710	0.015	63.9	60.7

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