基于几丁质酶3样蛋白的乙肝病毒感染免疫耐受期肝脏严重程度预测模型

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2025-02-01 Epub Date: 2024-12-10 DOI:10.1016/j.cca.2024.120085
Jia-Lan Wang, Su-Wen Jiang, Ai-Rong Hu, Xiao-Jun Shi, Ai-Wu Zhou, Ken Lin, Ying Fan, Meng-Han Jin, Hao-Jin Zhang
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引用次数: 0

摘要

背景:慢性乙型肝炎(CHB)患者是否在免疫耐受期(IT)治疗是一个持续争论的问题,因为很难区分不同程度的肝脏疾病严重程度。我们创建并评估了一种新的诊断模型,用于识别IT期CHB患者的显著肝组织损伤。方法:对2018年11月至2022年12月在宁波市第二医院行肝活检的311例慢性乙型肝炎病毒感染(HBV DNA > 30 IU/mL)患者进行横断面研究,其中IT期44例。利用单变量回归分析和logistic分析,建立并验证了预测CHB患者和IT期肝脏炎症和纤维化严重程度的模型。结果:几丁质酶3样蛋白(CHI3L1)、白蛋白(ALB)、丙氨酸转氨酶(ALT) /天冬氨酸转氨酶(AST)是CHB合并IT患者肝脏损害严重程度的独立预测因子。将三者结合构建模型(命名为CAA指数),结果显示出良好的性能。CAA指数达到受试者工作特征曲线下面积(AUC) 0.916(95 % CI, 0.820-1.000),验证组AUC为0.875(95 % CI, 0.683-1.000)。结论:CHI3L1可作为慢性乙型肝炎患者肝纤维化和炎症的独立指标。该诊断模型对评估患者肝脏病变严重程度有一定价值,可能是一种可靠的非侵入性诊断模型,有助于确定IT期CHB患者是否需要治疗。
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A model based on chitinase 3-like protein for expecting liver severity of hepatitis B virus infections in the immune tolerance phase.

Background: The question of whether to treat patients with chronic hepatitis B (CHB) during the immune tolerance (IT) period is a matter of ongoing debate, as it is difficult to discern different levels of liver disease severity. We created and assessed a novel diagnostic model for identifying significant liver tissue damage in individuals with CHB in IT phase.

Methods: From November 2018 to December 2022, a cross-sectional study of 311 patients with chronic hepatitis B virus infection (HBV DNA > 30 IU/mL) at Ningbo No. 2 Hospital, Ningbo, China, who underwent liver biopsy, including 44 patients in IT phase. Utilizing univariate regression analyses and logistics analysis, and model was developed and validated to predict the severity of hepatic inflammatory and fibrosis in CHB patients and in IT phase.

Results: Chitinase 3-like Protein (CHI3L1), albumin (ALB), alanine transaminase (ALT) / aspartate aminotransferase (AST) were identified as independent predictors of liver lesion severity in CHB patients with IT. The three were combined to build the model (named as CAA index), which demonstrated good performance. The CAA index achieved an area under the receiver operating characteristic curve (AUC) of 0.916 (95 % CI, 0.820-1.000) and AUC of validation group was 0.875 (95 % CI, 0.683-1.000).

Conclusions: CHI3L1 serves as an independent measure of liver fibrosis and inflammation in CHB. This diagnostic model has some value in assessing the severity of the patient's liver lesion severity and may be a reliable non-invasive diagnostic model helping determine whether treatment is necessary among CHB patients in IT phase.

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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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