Inadomi Chika, Takahashi Hirokazu, Ogawa Yuji, Oeda Satoshi, Imajo Kento, Kubotsu Yoshihito, Tanaka Kenichi, Kessoku Takaomi, Okada Michiaki, Isoda Hiroshi, Akiyama Takumi, Fukushima Hideaki, Yoneda Masato, Anzai Keizo, Aishima Shinichi, Nakajima Atsushi, Eguchi Yuichiro
Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
Liver Center, Saga University Hospital, Saga, Japan.
Hepatol Res. 2020 Jun;50(6):682-692. doi: 10.1111/hepr.13495. Epub 2020 Mar 25.
The Enhanced Liver Fibrosis (ELF) test comprises a logarithmic algorithm combining three serum markers of hepatic extracellular matrix metabolism. We aimed to evaluate the performance of ELF for the diagnosis of liver fibrosis and to compare it with that of liver stiffness measurement (LSM) by FibroScan in non-alcoholic fatty liver disease.
ELF cut-off values for the diagnosis of advanced fibrosis were obtained using receiver operating characteristic analysis in patients with biopsy-confirmed non-alcoholic fatty liver disease (training set; n = 200). Diagnostic performance was analyzed in the training set and in a validation set (n = 166), and compared with that of LSM in the FibroScan cohort (n = 224).
The area under receiver operating characteristic curve was 0.81 for the diagnosis of advanced fibrosis, and the ELF cut-off values were 9.34 with 90.4% sensitivity and 10.83 with 90.6% specificity in the training set, and 89.8% sensitivity and 85.5% specificity in the validation set. There was no significant difference in the area under the receiver operating characteristic curve between ELF and LSM (0.812 and 0.839). A combination of ELF (cut-off 10.83) and LSM (cut-off 11.45) increased the specificity to 97.9% and the positive predictive value, versus ELF alone. Sequential use of the Fibrosis-4 index (cut-off 2.67) and ELF (cut-off 9.34) increased the sensitivity to 95.9%.
ELF can identify advanced liver fibrosis in non-alcoholic fatty liver disease, and its diagnostic accuracy is comparable to that of FibroScan. According to the clinical setting, combinations or sequential procedures using other non-invasive tests complement the diagnostic performance of ELF for the identification of advanced fibrosis.
增强肝纤维化(ELF)检测包含一种对数算法,该算法结合了三种肝细胞外基质代谢的血清标志物。我们旨在评估ELF在非酒精性脂肪性肝病中诊断肝纤维化的性能,并将其与FibroScan测量的肝脏硬度值(LSM)进行比较。
在经活检证实的非酒精性脂肪性肝病患者(训练集;n = 200)中,使用受试者工作特征分析获得ELF诊断晚期纤维化的临界值。在训练集和验证集(n = 166)中分析诊断性能,并与FibroScan队列(n = 224)中的LSM进行比较。
诊断晚期纤维化时,受试者工作特征曲线下面积为0.81,训练集中ELF临界值为9.34时敏感性为90.4%,临界值为10.83时特异性为90.6%,验证集中敏感性为89.8%,特异性为85.5%。ELF和LSM的受试者工作特征曲线下面积无显著差异(分别为0.812和0.839)。与单独使用ELF相比,ELF(临界值10.83)和LSM(临界值11.45)联合使用可将特异性提高至97.9%,并提高阳性预测值。依次使用纤维化-4指数(临界值2.67)和ELF(临界值9.34)可将敏感性提高至95.9%。
ELF可识别非酒精性脂肪性肝病中的晚期肝纤维化,其诊断准确性与FibroScan相当。根据临床情况,使用其他非侵入性检测的联合或序贯程序可补充ELF在识别晚期纤维化方面的诊断性能。