非酒精性脂肪肝非侵入性诊断试验的诊断准确性：系统评价和网络荟萃分析

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S501445

Yuxin Sun, Die Hu, Mingkun Yu, Shi-Bing Liang, Youyou Zheng, Xin Wang, Guangdong Tong

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引用次数: 0

摘要

目的：近几十年来，许多用于诊断非酒精性脂肪性肝病（NAFLD）的非侵入性检查（nit）已经被开发出来，然而，缺乏对其相对诊断准确性的全面比较。我们的目的是利用网络荟萃分析（NMA）评估和比较各种nit对NAFLD的诊断准确性。材料和方法：截至2024年4月，我们对7个数据库进行了系统检索，以肝活检为金标准，确定评估nit诊断价值的研究。参与者包括疑似或确诊的NAFLD患者，不分年龄、性别、种族。贝叶斯NMA采用R 4.0.3进行统计分析，两两meta分析采用STATA 17.0进行统计分析。计算敏感性、特异性、诊断优势比（DOR）、受试者工作特征曲线下面积（AUC）和优势指数。使用Rstan包执行贝叶斯计算，指定MCMC链计数、迭代计数和操作周期等参数。使用QUADAS-2工具评估纳入研究的方法学质量。结果：在15877项研究中，180项被纳入定量综合，102项被用于头对头meta分析。对于脂肪变性1期的诊断，氢磁共振波谱（H-MRS, DOR 15,745,657.6, 95% CI 17.2-1,014,063.59）被证明是最准确的。对于严重纤维化，HRI领先（DOR 80.94, 95% CI 6.46-391.41），对于晚期纤维化，CK-18表现出最高的性能（DOR 102654.16, 95% CI 1.6-134,059.8）。对于高风险NASH，实时弹性成像显示出最高的性能（DOR 18.1, 95% CI 0.7-96.33）。荟萃回归分析表明，NITs对NAFLD诊断准确性的差异可能是由于研究设计、阈值、人群和性能指标的差异。结论：我们进行了网络荟萃分析，对这些测试的准确性进行排序。虽然一些结果很有希望，但并不是所有的nit都显示出相当高的准确性，这突出了需要用更大的数据集进行验证。未来的研究应集中在研究nit的阈值和提高方法报告的清晰度。

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Diagnostic Accuracy of Non-Invasive Diagnostic Tests for Nonalcoholic Fatty Liver Disease: A Systematic Review and Network Meta-Analysis.

Purpose: In recent decades, numerous non-invasive tests (NITs) for diagnosing nonalcoholic fatty liver disease (NAFLD) have been developed, however, a comprehensive comparison of their relative diagnostic accuracies is lacking. We aimed to assess and compare the diagnostic accuracy of various NITs for NAFLD using network meta-analysis (NMA).

Materials and methods: We conducted a systematic search in seven databases up to April 2024 to identify studies evaluating the diagnostic values of NITs, with liver biopsy as the gold standard. The participants included patients with suspected or confirmed NAFLD, irrespective of age, sex, ethnicity. Statistical analysis was conducted using R 4.0.3 for Bayesian NMA and STATA 17.0 for pairwise meta-analysis. Sensitivity, specificity, diagnostic odds ratio (DOR), area under the receiver operating characteristic curve (AUC), and superiority index were calculated. Bayesian calculations were performed using the Rstan package, specifying parameters like MCMC chain count, iteration count, and operational cycles. The methodological quality of included studies was assessed using the QUADAS-2 tool.

Results: Out of 15,877 studies, 180 were included in the quantitative synthesis, and 102 were used in head-to-head meta-analyses. For diagnosing steatosis stage 1, Hydrogen Magnetic Resonance Spectroscopy (H-MRS, DOR 15,745,657.6, 95% CI 17.2-1,014,063.59) proved to be the most accurate. For significant fibrosis, HRI leading (DOR 80.94, 95% CI 6.46-391.41), For advanced fibrosis, CK-18 showed the highest performance (DOR 102654.16, 95% CI 1.6-134,059.8). For high-risk NASH, Real-Time Elastography showing the highest performance (DOR 18.1, 95% CI 0.7-96.33). Meta-regression analyses suggested that variability in the diagnostic accuracy of NITs for NAFLD may result from differences in study design, thresholds, populations, and performance indicators.

Conclusion: We conducted a network meta-analysis to rank the accuracy of these tests. While some results are promising, not all NITs demonstrate substantial accuracy, highlighting the need for validation with larger datasets. Future research should concentrate on studying the thresholds of NITs and enhancing the clarity of methodological reporting.

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.