Jin-Hua Liang1,2,3, Rui Gao4, Jun-Cheng Dai5, Robert Peter Gale6, Wang Li1,2,3, Lei Fan1,2,3, Zhi-Bin Hu5, Wei Xu7,8,9, Jian-Yong Li10,11,12. 1. Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. 2. Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China. 3. Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. 4. Department of Endocrinology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. 5. Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing, 210029, China. 6. Division of Experimental Medicine, Department of Medicine, Haematology Research Centre, Imperial College London, London, UK. 7. Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. xuwei10000@hotmail.com. 8. Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China. xuwei10000@hotmail.com. 9. Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. xuwei10000@hotmail.com. 10. Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. lijianyonglm@medmail.com.cn. 11. Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China. lijianyonglm@medmail.com.cn. 12. Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. lijianyonglm@medmail.com.cn.
Abstract
PURPOSE: We attempt to assess the impact of hepatis-B virus (HBV) status on the prognosis of chronic lymphocytic leukemia (CLL) using a Chinese case cohort. METHODS: Five hundred and one consecutive newly diagnosed subjects with CLL were enrolled in this case cohort. HBV infection was defined as hepatitis B surface antigen (HBsAg) positive or hepatitis-B core antibody (HBcAb) positive. Univariate and stepwise multivariate Cox regression analyses were used to screen the prognostic risk factors associated with the end point of time-to-treatment (TTT) or overall survival (OS). Bootstrap re-sampling method was used to evaluate the model's internal validity. The discriminative ability of the models was evaluated using time-dependent receiver-operator characteristic (ROC) curves and corresponding areas under the curve (AUC). RESULTS: One hundred and twenty-one subjects (24%) among 501 patients were HBV positive. HBV infection was an independent predictor for the prognosis of TTT (HR = 1.37; 95% CI 1.04-1.80) or OS (HR =2.85; 95% CI 1.80-4.52). The AUCs for HBV infection were 0.62 (95% CI 0.58-0.66) for TTT and 0.69 (95% CI 0.66-0.72) for OS, respectively. When we combined HBV infection with the traditional clinical and biological factors, significant improvements for model's discrimination were observed for TTT [AUC: 0.81 (95% CI: 0.77-0.85) vs. 0.78 (95% CI: 0.74-0.82), P < 0.001] and OS [AUC: 0.81 (95% CI 0.76-0.86) vs. 0.76 (95% CI 0.71-0.82), P < 0.001). Further bootstrap re-sampling method revealed good internal consistence for the final optimal models (Average AUC: 0.78 for TTT and 0.79 for OS based on 1000 bootstraps). CONCLUSIONS: Our results indicated that HBV infection should be served as an important risk predictor for prognosis of CLL (TTT and OS).
PURPOSE: We attempt to assess the impact of hepatis-B virus (HBV) status on the prognosis of chronic lymphocytic leukemia (CLL) using a Chinese case cohort. METHODS: Five hundred and one consecutive newly diagnosed subjects with CLL were enrolled in this case cohort. HBV infection was defined as hepatitis B surface antigen (HBsAg) positive or hepatitis-B core antibody (HBcAb) positive. Univariate and stepwise multivariate Cox regression analyses were used to screen the prognostic risk factors associated with the end point of time-to-treatment (TTT) or overall survival (OS). Bootstrap re-sampling method was used to evaluate the model's internal validity. The discriminative ability of the models was evaluated using time-dependent receiver-operator characteristic (ROC) curves and corresponding areas under the curve (AUC). RESULTS: One hundred and twenty-one subjects (24%) among 501 patients were HBV positive. HBV infection was an independent predictor for the prognosis of TTT (HR = 1.37; 95% CI 1.04-1.80) or OS (HR =2.85; 95% CI 1.80-4.52). The AUCs for HBV infection were 0.62 (95% CI 0.58-0.66) for TTT and 0.69 (95% CI 0.66-0.72) for OS, respectively. When we combined HBV infection with the traditional clinical and biological factors, significant improvements for model's discrimination were observed for TTT [AUC: 0.81 (95% CI: 0.77-0.85) vs. 0.78 (95% CI: 0.74-0.82), P < 0.001] and OS [AUC: 0.81 (95% CI 0.76-0.86) vs. 0.76 (95% CI 0.71-0.82), P < 0.001). Further bootstrap re-sampling method revealed good internal consistence for the final optimal models (Average AUC: 0.78 for TTT and 0.79 for OS based on 1000 bootstraps). CONCLUSIONS: Our results indicated that HBV infection should be served as an important risk predictor for prognosis of CLL (TTT and OS).
Entities:
Keywords:
Chronic lymphocytic leukemia; HBV; Overall survival; Prognostic biomarker; Time to first treatment
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