FibroScan-AST评分对具有显著活动性和纤维化的非酒精性脂肪性肝炎患者的诊断效能
DOI: 10.3969/j.issn.1001-5256.2022.06.014
Diagnostic accuracy of FibroScan-AST score in nonalcoholic steatohepatitis with significant activity and fibrosis
-
摘要:
目的 验证FibroScan-AST(FAST)评分对非酒精性脂肪性肝病(NAFLD)活动性积分升高(NAS≥4)和显著纤维化(F≥2)的非酒精性脂肪性肝炎(高危NASH)患者的诊断效能,并与其他血清学模型进行比较。 方法 回顾性纳入2015年1月—2020年12月在上海交通大学医学院附属瑞金医院住院并经肝组织病理学证实为NAFLD/NASH患者84例,患者于肝活检前后1周内完成FibroScan检查(包括肝脏硬度值和受控衰减参数)和血清生化指标检测。多组间比较采用Kruskal-Wallis H秩和检验。Spearman相关系数(r)用于分析变量之间的相关性。以肝组织病理学检查结果为“金标准”,绘制受试者工作特征曲线(ROC曲线),计算曲线下面积(AUC)。根据既往研究确定的分界值,计算灵敏度、特异度、阳性预测值(PPV)和阴性预测值(NPV)和分类准确性。亚组分析时,采用不同临床指标分组进而比较各模型的评估效能,用AUC(95%CI)表示。 结果 84例患者中有高危NASH患者43例。所有患者FAST是0.54(0.04~0.93),F0~4不同肝纤维化分期的FAST依次为0.26(0.06~0.73)、0.48(0.04~0.82)、0.61(0.13~0.75)、0.64(0.09~0.93)和0.82(0.75~0.89),差异有统计学意义(H=23.360,P<0.001)。FAST与肝纤维化分期呈正相关(r=0.491,P<0.001)。NFS、FIB-4和APRI与肝纤维化分期均呈正相关(r值分别为0.230、0.346和0.281,P值均<0.05),但较FAST相关性减弱。FAST评估高危NASH的AUC为0.725(95%CI:0.617~0.834,P<0.001)。按既往研究确定的低分界值FAST≤0.35可使21例(25%)患者以71%的NPV来排除高危NASH。按高分界值FAST≥0.67可使19例(22.6%)以74%的PPV来确诊高危NASH。NFS和FIB-4诊断高危NASH的AUC分别为0.633(95%CI:0.513~0.753)和0.686(95%CI: 0.570~0.803),P值均<0.05。 结论 FAST可较准确评估那些伴或不伴代谢危险因素的NAFLD患者是否存在高危NASH,选择合适的分界值可使部分患者避免进行肝活检。 Abstract:Objective To investigate the diagnostic accuracy of FibroScan-AST (FAST) score in patients with high-risk nonalcoholic steatohepatitis (NASH) with a nonalcoholic fatty liver disease (NAFLD) activity score of ≥4 and significant liver fibrosis (F ≥2), along with comparison with other serological models. Methods A total of 84 consecutively admitted patients hospitalized in Ruijin Hospital from January 2015 to December 2020 and biopsy-confirmed NAFLD/NASH were included in this study, and FibroScan (liver stiffness measurement and controlled attenuation parameter) and blood biochemical tests were performed at one week before and after liver biopsy. A Kruskal-Wallis H analysis of variance was used for comparison between multiple groups, and Spearman's correlation coefficient was used to analyze the correlation between variables. The receiver operating characteristic (ROC) curve was plotted with pathological results as the "gold standard", and the area under the ROC curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and classification accuracy were calculated based on the cut-off values determined by previous studies. In subgroup analysis, the patients were divided into subgroups based on different clinical indices to evaluate the diagnostic accuracy of each model, which was expressed as AUC (95% confidence interval [CI]). Results Among the 84 patients, 43 had high-risk NASH. The FAST score was 0.54(0.04-0.93) for all patients, and the FAST score for liver fibrosis stages F0-F4 was 0.26(0.06-0.73), 0.48(0.04-0.82), 0.61(0.13-0.75), 0.64(0.09-0.93), and 0.82(0.75-0.89), respectively, with a significant difference between stages (H=23.360, P < 0.001). FAST score was positively correlated with liver fibrosis stage (r=0.491, P < 0.001). NAFLD fibrosis score (NFS), fibrosis-4 (FIB-4), and aspartate aminotransferase-to-platelet ratio index were positively correlated with liver fibrosis stage (r=0.230, 0.346, and 0.281, all P < 0.05), with a weaker correlation than FAST score. FAST score had an AUC of 0.725 (95%CI: 0.617-0.834, P < 0.001) in evaluating high-risk NASH. According to the low cut-off value determined by previous studies, FAST score ≤0.35 excluded high-risk NASH in 21 patients (25%) with an NPV of 71%; according to the high cut-off value, FAST score ≥0.67 helped to make a confirmed diagnosis of high-risk NASH in 19 patients (22.6%) with a PPV of 74%. NFS and FIB-4 had an AUC of 0.633(95%CI: 0.513-0.753) and 0.686(95%CI: 0.570-0.803), respectively, in the diagnosis of high-risk NASH (P < 0.05). Conclusion FAST score can accurately determine the presence or absence of high-risk NASH in NAFLD patients with or without metabolic risk factors, and selection of appropriate cut-off values can help some patients avoid liver biopsy. -
表 1 FAST及其他模型排除或确诊高危NASH的效能
Table 1. Efficacy of FAST and other models for excluding or diagnosing high-risk NASH
分区 FAST NFS FIB-4 排除区间 分界值 ≤0.35 <-1.455 <1.3 例数(%) 21(25.0) 60(71.4) 35(41.7) 灵敏度 0.86(37/43) 0.40(17/43) 0.72(31/43) 特异度 0.37(15/41) 0.83(34/41) 0.56(23/41) NPV 0.71(15/21) 0.57(34/60) 0.66(23/35) 灰区 分界值 0.35~0.67 -1.455~0.676 1.3~2.67 例数(%) 44(52.4) 22(26.2) 35(41.7) 确诊区间 分界值 ≥0.67 ≥0.676 ≥2.67 例数(%) 19(22.6) 2(2.4) 14(16.7) 特异度 0.88(36/41) 1.00(41/41) 0.93(38/41) 灵敏度 0.33(14/43) 0.05(2/43) 0.26(11/43) PPV 0.74(14/19) 1.00(2/2) 0.79(11/14) 表 2 FAST及其他模型排除或确诊高危NASH的准确性
Table 2. Accuracy of FAST and other models for excluding or diagnosing high-risk NASH
分区 FAST NFS FIB-4 排除区间 分界值 ≤0.35 <-1.455 <1.3 例数(%) 21(25.0) 60(71.4) 35(41.7) 正确分类[例(%)] 15(71.4) 34(56.7) 23(65.7) 交界分类[例(%)] 4(19.0) 17(28.3) 8(22.9) 错误分类[例(%)] 2(9.5) 9(15.0) 4(11.4) 灰区 例数(%) 44(52.4) 22(26.2) 35(41.7) 高危[例(%)] 23(52.3) 15(68.2) 20(57.1) 边缘高危[例(%)] 17(38.6) 4(18.2) 9(25.7) 非高危[例(%)] 4(9.1) 3(13.6) 6(17.1) 确诊区间 分界值 ≥0.67 ≥0.676 ≥2.67 例数(%) 19(22.6) 2(2.4) 14(16.7) 正确分类[例(%)] 14(73.7) 2(100) 11(78.6) 交界分类[例(%)] 4(21.1) 0 3(21.4) 错误分类[例(%)] 1(5.3) 0 0 表 3 FAST及其他模型诊断高危NASH的亚组分析
Table 3. Subgroup analysis of FAST and other models for high-risk NASH diagnosis
类别 AUC(95%CI) FAST NFS FIB-4 年龄 <45岁 0.690(0.504~0.876) 0.403(0.219~0.587) 0.470(0.272~0.667) ≥45岁 0.798(0.661~0.934) 0.774(0.632~0.916) 0.786(0.646~0.925) 性别 男 0.746(0.552~0.941) 0.598(0.374~0.822) 0.641(0.433~0.849) 女 0.717(0.577~0.858) 0.626(0.477~0.776) 0.696(0.552~0.840) BMI <28 kg/m2 0.703(0.576~0.830) 0.654(0.516~0.792) 0.677(0.542~0.812) ≥28 kg/m2 0.788(0.561~1.000) 0.505(0.243~0.767) 0.717(0.475~0.960) AST <40 U/L 0.764(0.570~0.959) 0.429(0.184~0.673) 0.705(0.573~0.836) ≥40 U/L 0.750(0.623~0.876) 0.705(0.573~0.836) 0.707(0.573~0.841) 2型糖尿病 无 0.712(0.590~0.833) 0.645(0.515~0.776) 0.659(0.530~0.789) 有 0.776(0.500~1.000) 0.673(0.369~0.978) 0.837(0.576~1.000) -
[1] ZHOU F, ZHOU J, WANG W, et al. Unexpected rapid increase in the burden of NAFLD in China from 2008 to 2018: A systematic review and meta-analysis[J]. Hepatology, 2019, 70(4): 1119-1133. DOI: 10.1002/hep.30702. [2] KLEINER DE, BRUNT EM, WILSON LA, et al. Association of histologic disease activity with progression of nonalcoholic fatty liver disease[J]. JAMA Netw Open, 2019, 2(10): e1912565. DOI: 10.1001/jamanetworkopen.2019.12565. [3] NEWSOME PN, SASSO M, DEEKS JJ, et al. FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: A prospective derivation and global validation study[J]. Lancet Gastroenterol Hepatol, 2020, 5(4): 362-373. DOI: 10.1016/S2468-1253(19)30383-8. [4] ANSTEE QM, LAWITZ EJ, ALKHOURI N, et al. Noninvasive tests accurately identify advanced fibrosis due to NASH: Baseline data from the STELLAR trials[J]. Hepatology, 2019, 70(5): 1521-1530. DOI: 10.1002/hep.30842. [5] SIDDIQUI MS, YAMADA G, VUPPALANCHI R, et al. Diagnostic accuracy of noninvasive fibrosis models to detect change in fibrosis stage[J]. Clin Gastroenterol Hepatol, 2019, 17(9): 1877-1885. e5. DOI: 10.1016/j.cgh.2018.12.031. [6] KLEINER DE, BRUNT EM, van NATTA M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease[J]. Hepatology, 2005, 41(6): 1313-1321. DOI: 10.1002/hep.20701. [7] HARRISON SA, RATZIU V, BOURSIER J, et al. A blood-based biomarker panel (NIS4) for non-invasive diagnosis of non-alcoholic steatohepatitis and liver fibrosis: A prospective derivation and global validation study[J]. Lancet Gastroenterol Hepatol, 2020, 5(11): 970-985. DOI: 10.1016/S2468-1253(20)30252-1. [8] EDDOWES PJ, SASSO M, ALLISON M, et al. Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease[J]. Gastroenterology, 2019, 156(6): 1717-1730. DOI: 10.1053/j.gastro.2019.01.042. [9] OEDA S, TAKAHASHI H, IMAJO K, et al. Diagnostic accuracy of FibroScan-AST score to identify non-alcoholic steatohepatitis with significant activity and fibrosis in Japanese patients with non-alcoholic fatty liver disease: Comparison between M and XL probes[J]. Hepatol Res, 2020, 50(7): 831-839. DOI: 10.1111/hepr.13508. [10] POYNARD T, MUNTENAU M, MORRA R, et al. Methodological aspects of the interpretation of non-invasive biomarkers of liver fibrosis: A 2008 update[J]. Gastroenterol Clin Biol, 2008, 32(6 Suppl 1): 8-21. DOI: 10.1016/S0399-8320(08)73990-3. [11] BRIL F, MCPHAUL MJ, CAULFIELD MP, et al. Performance of plasma biomarkers and diagnostic panels for nonalcoholic steatohepatitis and advanced fibrosis in patients with type 2 diabetes[J]. Diabetes Care, 2020, 43(2): 290-297. DOI: 10.2337/dc19-1071.
计量
- 文章访问数: 1013
- HTML全文浏览量: 380
- PDF下载量: 125
- 被引次数: 0