Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan
Citation: Takayama T, Ebinuma H, Tada S, Yamagishi Y, Wakabayashi K, et al. (2011) Prediction of Effect of Pegylated Interferon Alpha-2b plus Ribavirin Combination Therapy in Patients with Chronic Hepatitis C Infection. PLoS ONE 6(12): e27223. doi:10.1371/journal.pone.0027223
Editor: Sung Key Jang, Pohang University of Science and Technology, Korea, Republic of Received: July 21, 2011; Accepted: October 12, 2011; Published: December 2, 2011
Abstract
Treatment with pegylated interferon alpha-2b (PEGIFN) plus ribavirin (RBV) is standard therapy for patients with chronic hepatitis C. Although the effectiveness, patients with high titres of group Ib hepatitis C virus (HCV) respond poorly compared to other genotypes. At present, we cannot predict the effect in an individual. Previous studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. The aim of this study is to predict the effect of PEG IFN plus RBV therapy on an individual patient level using an artificial neural network system (ANN). 156 patients with HCV group 1b from multiple centres were treated with PEGIFN (1.5 µg/kg) plus RBV (400–1000 mg) for 48 weeks. Data on the patients' demographics, laboratory tests, PEGIFN, and RBV doses, early viral responses (EVR), and sustained viral responses were collected. Clinical data were randomly divided into training data set and validation data set and analyzed using multiple logistic regression analysis (MLRs) and ANN to predict individual outcomes. The sensitivities of predictive expression were 0.45 for the MLRs models and 0.82 for the ANNs and specificities were 0.55 for the MLR and 0.88 for the ANN. Non-linear relation analysis showed that EVR, serum creatinine, initial dose of Ribavirin, gender and age were important predictive factors, suggesting non-linearly related to outcome. In conclusion, ANN was more accurate than MLRs in predicting the outcome of PEGIFN plus RBV therapy in patients with group 1b HCV.
Discussion Only
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Interactions between clinical, genetic, and environmental factors may affect the efficacy of PEGIFN plus RBV combination therapy in CHC patients and should be taken into account by physicians when interpreting indications for therapy. Although there are reports on predictors of the response to treatment against the HCV [28], [29], data derived from clinical epidemiology studies and medical statistics do not always result in correct predictions at the level of the individual patient. For instance, both male sex and low total cholesterol level are considered indicative of a good prognosis [28], but the prognosis for a male who also has a high total cholesterol level is unknown. In contrast, ANNs can identify relationships within a patient's clinical data that may be overlooked when classical linear approaches are used [16]. MLRs are powerful tool to find significant factors and provide the key factors in present our study, though it is not suit to predict the results by using factors non-linearly correlate. Because ANNs are trained using existing data, they are more capable of providing correct answers for individual patients. The ANN also has theoretical advantages over conventional MLRs. Unlike MLRs, ANN can predict both linear and non-linear phenomena and can analyse relationships between many variables at different levels [25].
The incidence of correct answers and the AUC of the MLRs differed greatly from that of the ANN. Moreover, it can say that data used in most previous studies were not validated because input data sets were used to estimate ROCs. Therefore, we used validation data sets to estimate ROCs. If we had used only input data in the ANN, the AUROC would have been equal to 100% because an ANN can fit input data perfectly. Compared with MLRs, a well-trained ANN can predict both linear and non-linear data.
We note that, although the ANN is a useful model, the network logic of prediction cannot be broken down into simple elements because ANNs process data in a non-linear way [16], [18], [25], [30]–[32]. We analysed the relative weights of input factors to address this issue. The values of each factor affecting the outcome was analysed (figure 2). EVR was identified as the most important factor. Serum creatinine, initial dose of Ribavirin, gender and age also had high values (figure 2).
Both physicians and patients express concern about the risks associated with treatment because the outcome is difficult to predict at the time decisions are made. The increased demand for individualised treatment necessitates new statistics that can be applied in conjunction with ethical and clinical evidence at the individual level. ANNs also have potential economic benefits in that they reduce unnecessary medical treatment.
A report on the classification of patients was published recently [33]. Although this is a valid strategy, it is difficult to apply under clinical conditions because the ISDR mutant and Th1:Th2 ratio must first be determined. Moreover, there are some conflicting reports on the ISDR mutant [34]. As the aforementioned report did not performed validation, they should not be compared with our results; however, the predictive accuracy of our technique is superior to them.
The predictive expression developed in this study should aid physicians to advise individual patients on whether to continue with PEGIFN plus RBV combination therapy. We also tried to predict the effect only using pre-treatment parameters (table 8). Compare to the table 5, both sensitivity and specificity were dramatically improved by adding post-treatment parameter. Suggesting, post-treatment parameter such as adherence to treatment might affect to the effect of PEGIFN plus RBV combination therapy. As the EVR and total amount of RBV were the most important parameters in our study, the predictive expression could also be used to determine whether to increase the dose of RBV. Because we included the total amount of PEGIFN and RBV in the data sets, the effect of an increased dose can be simulated. Although the magnitude of the dose effect depends on patient's symptoms and exposure to adverse events, our technique remains a powerful tool for determining the appropriate dose of PEGIFN and RBV.
Although our predictive expression does not predict responses perfectly, our results show that the ANN is a valid method for devising individual treatment regimens in the clinical situation. It is well known that 100% prediction accuracy is impossible to achieve because of random error and multiple biases.
As the outcome of PEGIFN plus RBV treatment may be affected by multiple unknown factors, it is important to update data continuously and to acquire clinical data such as the patient's demographics, medical history, and laboratory test results. Recent accumulating data revealed the importance of IL28B gene from genome wide study [35]–[37]. Especially, very recent data clearly showed the significance of SNP rs12979860 in IL28B gene in the prediction of the treatment outcome [38]–[41]. We could not assess the effect of them in this study since we have not collected those data. Further analyses were needed though it may be improve the accuracy. It is also important to demonstrate that the use of trained ANNs in routine medical practice increases the quality of medical care and reduces costs.
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