A predictive model of response to peginterferon ribavirin in chronic hepatitis C using classification and regression tree analysis
A predictive model of response to peginterferon ribavirin in chronic hepatitis C using classification and regression tree analysis
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DOI：
10.1111/j.1872-034x.2009.00607.x
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发表时间：
2010-03-01
影响因子：
4.2
通讯作者：
Izumi, Namiki
中科院分区：
文献类型：
--
作者：
Kurosaki, Masayuki;Matsunaga, Kotaro;Izumi, Namiki
目的：在聚乙二醇干扰素联合利巴韦林治疗慢性丙型肝炎中，早期血清丙型肝炎病毒RNA的消失是实现持续病毒学应答(SVR)的先决条件。结果：CART分析确定肝脏脂肪变性(30%)是第一个预测应答的因素，其次是低密度脂蛋白胆固醇(LDLC)(=100 mg/dL)、年龄(50岁和60岁)、血糖(<；120 mg/dL)、伽马-谷氨酰转氨酶(<；40IU/L)，建立决策树模型。该模型包括7组不同的应答率，从低(15%)到高(77%)。模型的重复性经独立验证组验证(R2=0.987)。结论：建立包含肝脏脂肪变性、低密度脂蛋白胆固醇、年龄、血糖和GGT的决策树模型，可用于预测干扰素联合RBV治疗前的疗效，为临床选择治疗方案提供依据，并为代谢因素的治疗提供理论依据。
Aim:Early disappearance of serum hepatitis C virus (HCV) RNA is the prerequisite for achieving sustained virological response (SVR) in peg-interferon (PEG-IFN) plus ribavirin (RBV) therapy for chronic hepatitis C. This study aimed to develop a decision tree model for the pre-treatment prediction of response.Methods:Genotype 1b chronic hepatitis C treated with PEG-IFN alpha-2b and RBV were studied. Predictive factors of rapid or complete early virological response (RVR/cEVR) were explored in 400 consecutive patients using a recursive partitioning analysis, referred to as classification and regression tree (CART) and validated.Results:CART analysis identified hepatic steatosis (< 30%) as the first predictor of response followed by low-density-lipoprotein cholesterol (LDL-C) (>= 100 mg/dL), age (< 50 and < 60 years), blood sugar (< 120 mg/dL), and gamma-glutamyltransferase (GGT) (< 40 IU/L) and built decision tree model. The model consisted of seven groups with variable response rates from low (15%) to high (77%). The reproducibility of the model was confirmed by the independent validation group (r2 = 0.987). When reconstructed into three groups, the rate of RVR/cEVR was 16% for low probability group, 46% for intermediate probability group and 75% for high probability group.Conclusions:A decision tree model that includes hepatic steatosis, LDL-C, age, blood sugar, and GGT may be useful for the prediction of response before PEG-IFN plus RBV therapy, and has the potential to support clinical decisions in selecting patients for therapy and may provide a rationale for treating metabolic factors to improve the efficacy of antiviral therapy.