开发一种基于瞬时弹性成像（Fibroscan）和血清学检测公式的无创算法，用于诊断慢性乙型肝炎的晚期肝纤维化。
Development of a non-invasive algorithm with transient elastography (Fibroscan) and serum test formula for advanced liver fibrosis in chronic hepatitis B.
机构信息
Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.
出版信息
Aliment Pharmacol Ther. 2010 May;31(10):1095-103. doi: 10.1111/j.1365-2036.2010.04276.x. Epub 2010 Feb 23.
BACKGROUND
Non-invasive assessments of liver fibrosis in chronic hepatitis B were well established.
AIM
To develop a combined algorithm of liver stiffness measurement (LSM) and serum test formula to predict advanced liver fibrosis in chronic hepatitis B.
METHODS
We reported an alanine aminotransferase (AST)-based LSM algorithm for liver fibrosis in 156 chronic hepatitis B patients, which formed the training cohort to evaluate the performance of APRI (AST-to-platelet-ratio-index), Forns index, FIB-4 and Fibroindex against liver histology. The best combined LSM-serum formula algorithm would be validated in another cohort of 82 chronic hepatitis B patients.
RESULTS
In the training cohort, LSM has the best performance of diagnosing advanced (> or =F3) fibrosis [area under the receiver operating characteristics curve (AUROC) 0.88, 95% confidence interval (CI) 0.85-0.91], while Forns index has the best performance among the various serum test formulae (AUROC 0.70, 95% CI 0.62-0.78). In the combined algorithm, low LSM or low Forns index could be used to exclude advanced fibrosis as both of them had high sensitivity (>90%). To confirm advanced fibrosis, agreement between high LSM and high Forns index could improve the specificity (from 99% to 100% and from 87% to 98% in the training and validation cohorts respectively).
CONCLUSION
A combined LSM-Forns algorithm can improve the accuracy to predict advanced liver fibrosis in chronic hepatitis B.
背景
慢性乙型肝炎的肝纤维化无创评估已经得到了很好的建立。
目的
开发一种联合肝硬度测量（LSM）和血清检测公式的算法，以预测慢性乙型肝炎的晚期肝纤维化。
方法
我们报道了一种基于丙氨酸氨基转移酶（AST）的 LSM 算法，用于评估 156 例慢性乙型肝炎患者的肝纤维化，该算法构成了训练队列，以评估 APRI（AST-血小板比值指数）、Forns 指数、FIB-4 和 Fibroindex 对肝组织学的性能。最佳的联合 LSM-血清公式算法将在另外 82 例慢性乙型肝炎患者的队列中进行验证。
结果
在训练队列中，LSM 在诊断晚期（≥F3）纤维化方面表现最佳[受试者工作特征曲线（ROC）下面积（AUROC）为 0.88，95%置信区间（CI）为 0.85-0.91]，而各种血清检测公式中，Forns 指数表现最佳（AUROC 为 0.70，95%CI 为 0.62-0.78）。在联合算法中，低 LSM 或低 Forns 指数可用于排除晚期纤维化，因为它们的敏感性均高于 90%。为了确认晚期纤维化，高 LSM 和高 Forns 指数之间的一致性可以提高特异性（在训练和验证队列中分别从 99%提高到 100%和从 87%提高到 98%）。
结论
联合 LSM-Forns 算法可以提高预测慢性乙型肝炎晚期肝纤维化的准确性。
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