Khattab Mahmoud, Sakr Mohamed Amin, Fattah Mohamed Abdel, Mousa Youssef, Soliman Elwy, Breedy Ashraf, Fathi Mona, Gaber Salwa, Altaweil Ahmed, Osman Ashraf, Hassouna Ahmed, Motawea Ibrahim
Mahmoud Khattab, Mohamed Abdel Fattah, Youssef Mousa, Elwy Soliman, Ibrahim Motawea, Department of Internal Medicine, Minia University, Minia 61111, Egypt.
World J Hepatol. 2016 Nov 18;8(32):1392-1401. doi: 10.4254/wjh.v8.i32.1392.
To investigate the diagnostic ability of a non-invasive biological marker to predict liver fibrosis in hepatitis C genotype 4 patients with high accuracy.
A cohort of 332 patients infected with hepatitis C genotype 4 was included in this cross-sectional study. Fasting plasma glucose, insulin, C-peptide, and angiotensin-converting enzyme serum levels were measured. Insulin resistance was mathematically calculated using the homeostasis model of insulin resistance (HOMA-IR).
Fibrosis stages were distributed based on Metavir score as follows: F0 = 43, F1 = 136, F2 = 64, F3 = 45 and F4 = 44. Statistical analysis relied upon reclassification of fibrosis stages into mild fibrosis (F0-F) = 179, moderate fibrosis (F2) = 64, and advanced fibrosis (F3-F4) = 89. Univariate analysis indicated that age, log aspartate amino transaminase, log HOMA-IR and log platelet count were independent predictors of liver fibrosis stage ( < 0.0001). A stepwise multivariate discriminant functional analysis was used to drive a discriminative model for liver fibrosis. Our index used cut-off values of ≥ 0.86 and ≤ -0.31 to diagnose advanced and mild fibrosis, respectively, with receiving operating characteristics of 0.91 and 0.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio were: 73%, 91%, 75%, 90% and 8.0 respectively for advanced fibrosis, and 67%, 88%, 84%, 70% and 4.9, respectively, for mild fibrosis.
Our predictive model is easily available and reproducible, and predicted liver fibrosis with acceptable accuracy.
研究一种非侵入性生物标志物对丙型肝炎基因4型患者肝纤维化进行高精度预测的诊断能力。
本横断面研究纳入了332例丙型肝炎基因4型感染者。测量空腹血糖、胰岛素、C肽和血管紧张素转换酶血清水平。采用胰岛素抵抗稳态模型（HOMA-IR）进行胰岛素抵抗的数学计算。
根据梅塔维（Metavir）评分，纤维化阶段分布如下：F0 = 43，F1 = 136，F2 = 64，F3 = 45，F4 = 44。统计分析依赖于将纤维化阶段重新分类为轻度纤维化（F0-F1）= 179，中度纤维化（F2）= 64，重度纤维化（F3-F4）= 89。单因素分析表明，年龄、天冬氨酸转氨酶对数、HOMA-IR对数和血小板计数对数是肝纤维化阶段的独立预测因素（P < 0.0001）。采用逐步多元判别函数分析建立肝纤维化判别模型。我们的指标分别采用≥ 0.86和≤ -0.31的临界值来诊断重度和轻度纤维化，其受试者工作特征曲线下面积分别为0.91和0.88。重度纤维化的敏感性、特异性、阳性预测值、阴性预测值和阳性似然比分别为：73%、91%、75%、90%和8.0，轻度纤维化分别为：67%、88%、84%、70%和4.9。
我们的预测模型易于获得且可重复，能够以可接受的准确度预测肝纤维化。
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