Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong.
PLoS One. 2011;6(8):e23077. doi: 10.1371/journal.pone.0023077. Epub 2011 Aug 11.
We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB) based on routinely available clinical parameters.
237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg)-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108) and validation group (n = 129). Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed.
Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP), a model to predict significant liver fibrosis (Ishak fibrosis score ≥3) was derived using the five best parameters (age, ALP, AST, AFP and platelet). Using the formula log(index+1) = 0.025+0.0031(age)+0.1483 log(ALP)+0.004 log(AST)+0.0908 log(AFP+1)-0.028 log(platelet), the PAPAS (Platelet/Age/Phosphatase/AFP/AST) index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC) curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN)] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA) index, AST/platelet ratio index (APRI), and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively). Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided.
The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients.
我们基于常规临床参数建立了一个用于预测慢性乙型肝炎（CHB）患者显著纤维化的模型。
237 例未经治疗的 CHB 患者（58.4%为乙型肝炎 e 抗原[HBeAg]阳性）接受了肝活检，这些患者被随机分为两组：训练组（n=108）和验证组（n=129）。对肝组织学进行纤维化评估。分析所有常见的人口统计学、病毒血清学、病毒载量和肝功能。
基于 12 个可用的临床参数（年龄、性别、HBeAg 状态、HBV DNA、血小板、白蛋白、胆红素、ALT、AST、ALP、GGT 和 AFP），使用五个最佳参数（年龄、ALP、AST、AFP 和血小板）得出了一个用于预测显著肝纤维化（Ishak 纤维化评分≥3）的模型。使用公式 log(index+1)=0.025+0.0031（年龄）+0.1483 log(ALP)+0.004 log(AST)+0.0908 log(AFP+1)-0.028 log(血小板），PAPAS（血小板/年龄/碱性磷酸酶/AFP/AST）指数预测显著纤维化的受试者工作特征曲线下面积（AUROC）为 0.776 [0.797 适用于 ALT<2×正常值上限（ULN）的患者]，阴性预测值排除显著纤维化的为 88.4%。这种预测能力优于其他使用常见参数的非侵入性模型，包括 AST/血小板/GGT/AFP（APGA）指数、AST/血小板比值指数（APRI）和 FIB-4 指数（AUROC 分别为 0.757、0.708 和 0.723）。使用 PAPAS 指数，可以避免对 ALT<2×ULN 的患者进行 67.5%的肝活检。
PAPAS 指数可用于预测和排除显著纤维化，可能减少 CHB 患者的肝活检需求。
