Predicting Discontinuation of Pegylated Interferon as a Result of Lack of Efficacy in United States Veterans With Chronic Hepatitis C on Dual Therapy.

ANNALS OF PHARMACOTHERAPY(2014)

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摘要
BACKGROUND:Many patients with chronic hepatitis C virus (HCV) being treated with pegylated interferon (peg-IFN) plus ribavirin (RBV) do not respond to therapy and do not clear the virus. Standard of care during the era of dual therapy was to discontinue the patient's therapy based on insufficient decreases in viral load after 12 and/or 24 weeks on therapy. OBJECTIVES:We identified patient characteristics that were significant predictors of discontinuation as a result of lack of efficacy (LOE) in a national database of US veterans with genotypes 1 and 4. METHODS:We identified US veterans who received care at Veterans Affairs medical centers in 2004-2009 and who had lab-confirmed HCV diagnoses and initiated therapy with peg-IFN plus RBV. Patients who discontinued therapy early were classified as either LOE or non-LOE discontinuers based on pharmacy refill patterns and laboratory response data. Predictors of LOE discontinuation were identified using univariate and multivariable Cox proportional hazards modeling. RESULTS:Of 321 238 HCV patients with an ICD-9 diagnosis code, 31 215 (9.7%) initiated dual therapy with peg-IFN plus RBV, and 10 333 (3.2%) met all inclusion criteria and were included in the analysis. Overall, 13.6% of the cohort was classified as LOE. Significant predictors of LOE discontinuation included treatment for drug abuse (hazard ratio [HR] = 2.18), age >65 years (HR = 1.75), antiretroviral therapy for HIV (HR = 1.48), black race (HR = 1.47), platelet count >100/mm3 (HR = 1.46), and drug therapy for insomnia (HR = 1.40). CONCLUSIONS:We identified risk factors for discontinuation caused by LOE. Future work should focus on determining whether these characteristics are also predictive of triple-therapy LOE discontinuations.
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关键词
hepatitis C virus,pegylated interferon,lack of efficacy,therapy discontinuation,veterans,predictive model
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