Low Serum Alpha-fetoprotein Level an Important Predictor for Therapeutic Outcome in Egyptian Patients with Chronic Hepatitis C: A Data-Mining Analysis

Abstract

Background Data mining can build predictive models for the response to antiviral therapy in chronic HCV patients. Objective To develop a prediction model for therapeutic outcome in chronic HCV genotype-4 patients using different decision-trees learning algorithms. Study Design Data of 3719 chronic HCV patients who had received PEG-IFN/RBV therapy at Cairo-Fatemia Hospital, Egypt was retrieved. Factors predictive of SVR were explored using data mining analysis. Weka implementations C4.5, classification and Reduced Error Pruning tree were constructed using 22 attributes from initial patients’ data. Results End of treatment response and estimated SVR were 61.6%, 52.5% respectively. Low median AFP; 2.9 ng/ml was significantly associated with SVR; compared to relapse group 5.06 ng/ml; p value<0.01. AFP was identified as the most decisive variable of initial split by both decision-tree models. Various cutoff levels were related to different probability of SVR. Baseline AFP ≤2.48 ng/ml was associated with 72%SVR while levels ≥ 7.8 ng/ml demonstrated 32%. Other attributes such as age, BMI, ALT, hepatic fibrosis and activity were less decisive in prediction of response. This was further confirmed by univariate logistic regression analysis; p value<0.01. Conclusion Low AFP Levels were significantly related to SVR in an HCV population presumably genotype-4 as demonstrated by data mining.