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Relevance of baseline viral genetic heterogeneity and host factors for treatment outcome prediction in hepatitis C virus 1b-infected patients.

Saludes V, Bascuñana E, Jordana-Lluch E, Casanovas S, Ardèvol M, Soler E, Planas R, Ausina V, Martró E - PLoS ONE (2013)

Bottom Line: Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV).The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients.Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

View Article: PubMed Central - PubMed

Affiliation: Microbiology Service, Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain ; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

ABSTRACT

Background: Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.

Methodology: Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.

Principal findings: A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively).

Conclusions and significance: The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

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Decision tree model generated in the training group.The factors used for splitting and their cut-offs are indicated. Pie charts represent the rate of sustained virological response in white (the percentage is indicated) for each group of patients after each split. nHap_E1E2, number of haplotypes in the E1–E2 studied region; ALT quotient, square root of the alanine transaminase levels expressed as factor times upper limit of normal used in our center for males and females (41 and 31 U/L, respectively).
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pone-0072600-g001: Decision tree model generated in the training group.The factors used for splitting and their cut-offs are indicated. Pie charts represent the rate of sustained virological response in white (the percentage is indicated) for each group of patients after each split. nHap_E1E2, number of haplotypes in the E1–E2 studied region; ALT quotient, square root of the alanine transaminase levels expressed as factor times upper limit of normal used in our center for males and females (41 and 31 U/L, respectively).

Mentions: The generated decision tree is shown in Figure 1. The variables that persisted in this predictive model in decreasing order of significance were: the IL28B polymorphism (G2 = 14.1257), the ALT ratio (G2 = 12.8909), the nHap_E1–E2 (G2 = 12.1293), and the Forns index (G2 = 6.6038). This model was able to predict treatment outcome accurately in the training group (AUROC = 0.9072, 84.4% specificity, 80.0% PPV, 95.2% sensitivity and 96.4% NPV) (Table 3). In the validation group these values decreased to 70% specificity, 75.0% PPV, 81.8% sensitivity and 77.8% NPV (AUROC = 0.7361).


Relevance of baseline viral genetic heterogeneity and host factors for treatment outcome prediction in hepatitis C virus 1b-infected patients.

Saludes V, Bascuñana E, Jordana-Lluch E, Casanovas S, Ardèvol M, Soler E, Planas R, Ausina V, Martró E - PLoS ONE (2013)

Decision tree model generated in the training group.The factors used for splitting and their cut-offs are indicated. Pie charts represent the rate of sustained virological response in white (the percentage is indicated) for each group of patients after each split. nHap_E1E2, number of haplotypes in the E1–E2 studied region; ALT quotient, square root of the alanine transaminase levels expressed as factor times upper limit of normal used in our center for males and females (41 and 31 U/L, respectively).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3755994&req=5

pone-0072600-g001: Decision tree model generated in the training group.The factors used for splitting and their cut-offs are indicated. Pie charts represent the rate of sustained virological response in white (the percentage is indicated) for each group of patients after each split. nHap_E1E2, number of haplotypes in the E1–E2 studied region; ALT quotient, square root of the alanine transaminase levels expressed as factor times upper limit of normal used in our center for males and females (41 and 31 U/L, respectively).
Mentions: The generated decision tree is shown in Figure 1. The variables that persisted in this predictive model in decreasing order of significance were: the IL28B polymorphism (G2 = 14.1257), the ALT ratio (G2 = 12.8909), the nHap_E1–E2 (G2 = 12.1293), and the Forns index (G2 = 6.6038). This model was able to predict treatment outcome accurately in the training group (AUROC = 0.9072, 84.4% specificity, 80.0% PPV, 95.2% sensitivity and 96.4% NPV) (Table 3). In the validation group these values decreased to 70% specificity, 75.0% PPV, 81.8% sensitivity and 77.8% NPV (AUROC = 0.7361).

Bottom Line: Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV).The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients.Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

View Article: PubMed Central - PubMed

Affiliation: Microbiology Service, Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain ; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

ABSTRACT

Background: Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.

Methodology: Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.

Principal findings: A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively).

Conclusions and significance: The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

Show MeSH