A scoring model predicts hepatitis B e antigen seroconversion in chronic hepatitis B patients treated with nucleos(t)ide analogs: real-world clinical practice. (September 2017)

• Record Type: Journal Article
• Authors: Geng, Mingfan; Li, Yuxin; Gao, Fangyuan; Sun, Le; Yang, Xue; Wang, Rui; Chen, Jialiang; Zhang, Qun; Wan, Gang; Wang, Xianbo
• Is Part Of: International journal of infectious diseases. Volume 62(2017:Sep.)
• Page Start: 18
• Page End: 25
• Publication Date: 2017-09
• Subjects: Chronic hepatitis B -- HBeAg seroconversion -- Nucleos(t)ide analogs; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2017.06.016
• Languages: English
• ISSNs: 1201-9712

Abstract

Abstract: Aim: This study developed and validated a non-invasive scoring model to predict 1-year hepatitis B e antigen (HBeAg) seroconversion in response to nucleos(t)ide analog (NA) treatment in NA-naïve patients with HBeAg-positive chronic hepatitis B (CHB). Methods: Baseline data from 1014 patients visiting the outpatient and inpatient clinics of Beijing Ditan Hospital, Capital Medical University, China between October 2008 and April 2015 were included. These patients received NAs for HBeAg-positive CHB. The patients were assigned randomly to the derivation ( n = 710) and validation ( n = 304) cohorts in a 7:3 ratio. A prediction scoring model was established based on univariate and multivariate Cox proportional hazards regression analyses to identify independent prediction factors. In the derivation cohort, the odds ratio of the predictors were converted to integer risk scores by rounding the quotient from dividing the odds ratio, and the final score was the sum of these values. The predictive accuracy of the scoring model was further assessed using Harrell's concordance index (C-index). Results: The 1-year cumulative HBeAg seroconversion rates were 11.83% and 8.55% in the derivation and validation cohorts, respectively. In the derivation cohort, baseline pretreatment alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (GGT), globulin (GLO), and quantitative HBeAg (qHBeAg) levels were independently associated with HBeAg seroconversion and were included in the scoring system. The model had good discrimination in the derivation and validation cohorts (C-index = 0.750, 95% confidence interval 0.694–0.806 and C-index = 0.776, 95% confidence interval 0.698–0.855, respectively). The prediction scores ranged from 0 to 4; scores of 0–1 and 2–4 identified patients with lower and higher levels of HBeAg seroconversion, respectively. Kaplan–Meier analysis was used to determine the 1-year cumulative HBeAg seroconversion rates in the two groups (scores of 0–1 and 2–4) of the primary cohort, and log-rank tests revealed a significant difference (4.87% vs. 20.9%, p < 0.0001). Conclusions: The 1-year prediction scoring model based on baseline levels of ALT, GGT, GLO, and qHBeAg offered a reliable predictive value for the response to NA therapy in a Chinese cohort.