AIM: To construct formulae for predicting the likelihood of ribavirin-induced anemia in pegylated interferon α plus ribavirin for chronic hepatitis C. METHODS: Five hundred and sixty-one Japanese patients with hepatitis C virus genotype 1b who had received combination treatment were enrolled and assigned randomly to the derivation and confirmatory groups. Single nucleotide polymorphisms at or nearby ITPA were genotyped by real-time detection polymerase chain reaction. Factors influencing significant anemia (hemoglobin concentration < 10.0 g/dL at week 4 of treatment) and significant hemoglobin decline (declining concentrations > 3.0 g/dL at week 4) were analyzed using multiple regression analyses. Prediction formulae were constructed by significantly independent factors. RESULTS: Multivariate analysis for the derivation group identified four independent factors associated with significant hemoglobin decline: hemoglobin decline at week 2 [P = 3.29 × 10(-17), odds ratio (OR) = 7.54 (g/dL)], estimated glomerular filtration rate [P = 2.16 × 10(-4), OR = 0.962 (mL/min/1.73 m(2))], rs1127354 (P = 5.75 × 10(-4), OR = 10.94) and baseline hemoglobin [P = 7.86 × 10(-4), OR = 1.50 (g/dL)]. Using the model constructed by these factors, positive and negative predictive values and predictive accuracy were 79.8%, 88.8% and 86.2%, respectively. For the confirmatory group, they were 83.3%, 91.0% and 88.3%. These factors were closely correlated with significant anemia. However, the model could not be constructed, because no patients with rs1127354 minor genotype CA/AA had significant anemia. CONCLUSION: Reliable formulae for predicting the likelihood of ribavirin-induced anemia were constructed. Such modeling may be useful in developing individual tailoring and optimization of ribavirin dosage.
RCT Entities:
AIM: To construct formulae for predicting the likelihood of ribavirin-induced anemia in pegylated interferon α plus ribavirin for chronic hepatitis C. METHODS: Five hundred and sixty-one Japanese patients with hepatitis C virus genotype 1b who had received combination treatment were enrolled and assigned randomly to the derivation and confirmatory groups. Single nucleotide polymorphisms at or nearby ITPA were genotyped by real-time detection polymerase chain reaction. Factors influencing significant anemia (hemoglobin concentration < 10.0 g/dL at week 4 of treatment) and significant hemoglobin decline (declining concentrations > 3.0 g/dL at week 4) were analyzed using multiple regression analyses. Prediction formulae were constructed by significantly independent factors. RESULTS: Multivariate analysis for the derivation group identified four independent factors associated with significant hemoglobin decline: hemoglobin decline at week 2 [P = 3.29 × 10(-17), odds ratio (OR) = 7.54 (g/dL)], estimated glomerular filtration rate [P = 2.16 × 10(-4), OR = 0.962 (mL/min/1.73 m(2))], rs1127354 (P = 5.75 × 10(-4), OR = 10.94) and baseline hemoglobin [P = 7.86 × 10(-4), OR = 1.50 (g/dL)]. Using the model constructed by these factors, positive and negative predictive values and predictive accuracy were 79.8%, 88.8% and 86.2%, respectively. For the confirmatory group, they were 83.3%, 91.0% and 88.3%. These factors were closely correlated with significant anemia. However, the model could not be constructed, because no patients with rs1127354 minor genotype CA/AA had significant anemia. CONCLUSION: Reliable formulae for predicting the likelihood of ribavirin-induced anemia were constructed. Such modeling may be useful in developing individual tailoring and optimization of ribavirin dosage.
Entities:
Keywords:
Chronic hepatitis C virus infection; Hemolytic anemia; Pegylated interferon α; Prediction model; Ribavirin; Single nucleotide polymorphism
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