School of Basic Medicine, Shaanxi University of Chinese Medicine, Xixian Avenue, Xixian New District, Xianyang, 712046, Shaanxi Province, China.
Xi'an Hospital of Traditional Chinese Medicine, Xi'an, 710016, Shaanxi, China.
BMC Med Inform Decis Mak. 2023 Dec 19;23(1):294. doi: 10.1186/s12911-023-02402-1.
Invasive detection methods such as liver biopsy are currently the gold standard for diagnosing liver cirrhosis and can be used to determine the degree of liver fibrosis and cirrhosis. In contrast, non-invasive diagnostic methods, such as ultrasonography, elastography, and clinical prediction scores, can prevent patients from invasiveness-related discomfort and risks and are often chosen as alternative or supplementary diagnostic methods for liver fibrosis or cirrhosis. However, these non-invasive methods cannot specify the pathological grading and early diagnosis of the lesions. Recent studies have revealed that gut microbiome-based machine learning can be utilized as a non-invasive diagnostic technique for liver cirrhosis or fibrosis, but there is no evidence-based support. Therefore, this study conducted a systematic review and meta-analysis for the first time to investigate the accuracy of machine learning based on the gut microbiota in the prediction of liver fibrosis and cirrhosis.
A comprehensive and systematic search of publications published before April 2th, 2023 in PubMed, Cochrane Library, Embase, and Web of Science was conducted for relevant studies on the application of gut microbiome-based metagenomic sequencing modeling technology to the diagnostic prediction of liver cirrhosis or fibrosis. A bivariate mixed-effects model and Stata software 15.0 were adopted for the meta-analysis.
Ten studies were included in the present study, involving 11 prediction trials and 838 participants, 403 of whom were fibrotic and cirrhotic patients. Meta-analysis showed the pooled sensitivity (SEN) = 0.81 [0.75, 0.85], specificity (SEP) = 0.85 [0.77, 0.91], positive likelihood ratio (PLR) = 5.5 [3.6, 8.7], negative likelihood ratio (NLR) = 0.23 [0.18, 0.29], diagnostic odds ratio (DOR) = 24 [14, 41], and area under curve (AUC) = 0.86 [0.83-0.89]. The results demonstrated that machine learning methods had excellent potential to analyze gut microbiome data and could effectively predict liver cirrhosis or fibrosis. Machine learning provides a powerful tool for non-invasive prediction and diagnosis of liver cirrhosis or liver fibrosis, with broad clinical application prospects. However, these results need to be interpreted with caution due to limited clinical data.
Gut microbiome-based machine learning can be utilized as a practical, non-invasive technique for the diagnostic prediction of liver cirrhosis or fibrosis. However, most of the included studies applied the random forest algorithm in modeling, so a diversified prediction system based on microorganisms is needed to improve the non-invasive detection of liver cirrhosis or fibrosis.
目前，肝活检等有创检测方法是诊断肝硬化的金标准，可用于确定肝纤维化和肝硬化的程度。相比之下，超声、弹性成像和临床预测评分等非侵入性诊断方法可以避免患者因侵袭性相关不适和风险而进行检测，通常被作为肝纤维化或肝硬化的替代或补充诊断方法。然而，这些非侵入性方法无法明确病变的病理分级和早期诊断。最近的研究表明，基于肠道微生物组的机器学习可作为一种用于诊断肝硬化或纤维化的非侵入性诊断技术，但缺乏循证支持。因此，本研究首次进行了系统评价和荟萃分析，以评估基于肠道微生物组的机器学习在预测肝纤维化和肝硬化中的准确性。
对 2023 年 4 月 2 日之前在 PubMed、Cochrane 图书馆、Embase 和 Web of Science 上发表的相关研究进行了全面、系统的检索，以评估基于肠道微生物组的宏基因组测序建模技术在诊断预测肝硬化或纤维化中的应用。采用双变量混合效应模型和 Stata 软件 15.0 进行荟萃分析。
本研究纳入了 10 项研究，涉及 11 项预测试验和 838 名参与者，其中 403 名参与者为纤维化和肝硬化患者。荟萃分析显示，合并敏感性（SEN）为 0.81 [0.75, 0.85]，特异性（SEP）为 0.85 [0.77, 0.91]，阳性似然比（PLR）为 5.5 [3.6, 8.7]，阴性似然比（NLR）为 0.23 [0.18, 0.29]，诊断比值比（DOR）为 24 [14, 41]，曲线下面积（AUC）为 0.86 [0.83-0.89]。结果表明，机器学习方法具有分析肠道微生物组数据的巨大潜力，可以有效地预测肝硬化或纤维化。机器学习为肝硬化或肝纤维化的非侵入性预测和诊断提供了一种强大的工具，具有广阔的临床应用前景。然而，由于临床数据有限，这些结果的解释需要谨慎。
基于肠道微生物组的机器学习可作为一种实用的非侵入性技术，用于诊断预测肝硬化或纤维化。然而，纳入的大多数研究都在建模中应用了随机森林算法，因此需要一个多样化的基于微生物的预测系统来提高对肝硬化或纤维化的非侵入性检测。
