Platelet count is associated with sustained virological response rates in treatments for chronic hepatitis C

Platelet count is associated with sustained virological response rates in treatments for chronic hepatitis C

The Korean Journal of Internal Medicine, March 2018

29529840

Baek Gyu Jun, Eui Ju Park, Woong Cheul Lee, Jae Young Jang, Soung Won Jeong, Young Don Kim, Gab Jin Cheon, Young Sin Cho, Sae Hwan Lee, Hong Soo Kim, Yun Nah Lee, Sang Gyune Kim, Young Seok Kim, Boo Sung Kim

This study was conducted to clarify the sustained virological response (SVR) prediction ability of baseline and treatment-related factors in patients with chronic hepatitis C virus (HCV) infection. This retrospective study collected data at four tertiary referral hospitals between June 2004 and July 2012. Out of 476 patients, 330 treatment-naïve patients with chronic HCV infection were recruited. Pegylated interferon α-2a/- 2b plus ribavirin was administered for either 24 or 48 weeks depending on the HCV genotype. The baseline and treatment-related predictive factors of SVR were evaluated by analyzing data measured before treatment (i.e., baseline) and during treatment. SVR rates for genotypes 1 and 2 were 63% (97/154) and 79.5% (140/176), respectively (p = 0.001). Multivariate analysis for baseline factors revealed that young age (p = 0.009), genotype 2 (p = 0.001), HCV RNA level of < 800,000 IU/mL (p < 0.001), and a baseline platelet count of > 150 × 103 /µL (p < 0.001) were significant SVR predictors, regardless of the genotype. In particular, predictive accuracy for achievement of SVR was 87.3% for a baseline platelet count of > 150 × 103 /µL. In multivariate analysis for treatment-related factors, SVR was associated with achievement of a rapid virological response (RVR; p < 0.001), treatment adherence of ≥ 80/80/80 (p < 0.001). Young age, genotype 2, low HCV RNA level, RVR, and treatment adherence were significantly associated with SVR. In addition, platelet count was an independent predictive factor for SVR. Therefore, platelet count could be used to develop individualized treatment regimens and to optimize treatment outcomes in patients with chronic HCV infection.