Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

Abstract

Background:Treatment with Peginterferon Alpha-2b plus Ribavirin is the current standard therapy for chronic hepatitis C (CHC). However, many host related and viral parameters are associated with different outcomes of combination therapy.Objectives:The aim of this study was to develop an artificial neural network (ANN) model to predetermine individual responses to therapy based on patient’s demographics and laboratory data.Patients and Methods:This case-control study was conducted in Tehran, Iran, on 139 patients divided into sustained virologic response (SVR) (n = 50), relapse (n = 50) and non-response (n = 39) groups according to their response to combination therapy for 48 weeks. The ANN was trained 300 times (epochs) using clinical data. To test the ANN performance, the part of data that was selected randomly and not used in training process was entered to the ANN and the outputs were compared with real data.Results:Hemoglobin (P < 0.001), cholesterol (P = 0.001) and IL-28b genotype (P = 0.002) values had significant differences between the three groups. Significant predictive factor(s) for each group were hemoglobin for SVR (OR: 1.517; 95% CI: 1.233-1.868; P < 0.001), IL-28b genotype for relapse (OR: 0.577; 95% CI: 0.339-0.981; P = 0.041) and hemoglobin (OR: 0.824; 95% CI: 0.693-0.980; P = 0.017) and IL-28b genotype (OR: 2.584; 95% CI: 1.430-4.668;P = 0.001) for non-response. The accuracy of ANN to predict SVR, relapse and non-response were 93%, 90%, and 90%, respectively.Conclusions:Using baseline laboratory data and host characteristics, ANN has been shown as an accurate model to predict treatment outcome, which can lead to appropriate decision making and decrease the frequency of ineffective treatment in patients with chronic hepatitis C virus (HCV) infection.

A Comprehensive Long-Term Prognosis of Chronic Hepatitis C Patients With Antiviral Therapy; A Meta-Analysis of Studies From 2008 to 2014

“…With the standard therapy, SVR rate might be different with different conditions or factors, while some areas and patients could achieve very high rates ( 7 , 8 ). Some predictors were found to indicate SVR possibility for patients; for example, interleukin-28b gene polymorphisms play a quiet role to predict treatment response ( 9 , 10 ). SVR predictors in the standard therapy had been deeply investigated; however, long-time outcome of SVR still needs to be further studied.…”

Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System

“…Intelligent artificial networks are also used to predict treatment outcomes in many diseases. [28][29][30][31][32][33] In this project, we used recommender system to predict which other diseases a chronic patient is susceptible for. We used three dimensional analytical algorithms using patient, disease, and time as its factors.…”

The role of hepatic progenitor cells in predicting response to therapy in Egyptian patients with chronic hepatitis C, genotype 4

“…The stage of liver fibrosis was claimed to be a good predictor of treatment response in HCV infection9,11. However, this claim could not be supported by others8,10,12 Last but not least, was the genotyping of IL-28 B which has been reported to be a strong predictor of treatment outcome in HCV patients10,13–15. However, estimation of this parameter needs sophisticated molecular techniques which are not usually available in general hospitals.…”

“…Several host and viral factors have been investigated to be used as markers to predict the response of chronic hepatitis C patients to the combined IFN and RBV treatment. Viral load was evaluated as a predictive factor by some studies with controversial results8–10 . The stage of liver fibrosis was claimed to be a good predictor of treatment response in HCV infection9,11.…”