Norah A Terrault1, Abdus S Wahed2, Jordan J Feld3, Stewart L Cooper4, Mark G Ghany5, Mauricio Lisker-Melman6, Robert Perrillo7, Richard K Sterling8, Mandana Khalili9, Raymond T Chung10, Philip Rosenthal11, Robert J Fontana12, Arif Sarowar3, Daryl T Y Lau13, Junyao Wang2, Anna S Lok14, Harry L A Janssen3. 1. Gastrointestinal and Liver Diseases Division, Keck Medicine of University of Southern California, Los Angeles, California, USA. 2. Department of Biostatistics and Epidemiology Data Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 3. Toronto Center for Liver Disease, University of Toronto, Toronto, Ontario, Canada. 4. San Francisco Center for Liver Disease, California Pacific Medical & Research Institute, San Francisco, California, USA. 5. Liver Diseases Branch, National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA. 6. Washington University School of Medicine and John Cochran VA Medical Center, St. Louis, Missouri, USA. 7. Baylor University Medical Center, Dallas, Texas, USA. 8. Section of Hepatology, Virginia Commonwealth University, Richmond, Virginia, USA. 9. Department of Medicine, University of California San Francisco, San Francisco, California, USA. 10. Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. 11. Department of Pediatrics, University of California San Francisco, San Francisco, California, USA. 12. Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA. 13. Liver Center, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts, USA. 14. Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA.
Abstract
BACKGROUND AND AIMS: Achieving HBsAg loss is an important landmark in the natural history of chronic hepatitis B (CHB). A more personalized approach to prediction of HBsAg loss is relevant in counseling patients. This study sought to develop and validate a prediction model for HBsAg loss based on quantitative HBsAg levels (qHBsAg) and other baseline characteristics. METHODS: The Hepatitis B Research Network (HBRN) is a prospective cohort including 1240 untreated HBeAg-negative patients (1150 adults, 90 children) with median follow-up of 5.5 years. Incidence rates of HBsAg loss and hepatitis B surface antibody (anti-HBs) acquisition were determined, and a predictor score of HBsAg loss using readily available variables was developed and externally validated. RESULTS: Crude incidence rates of HBsAg loss and anti-HBs acquisition were 1.6 and 1.1 per 100 person-years (PY); 67 achieved sustained HBsAg loss for an incidence rate of 1.2 per 100 PY. Increased HBsAg loss was significantly associated with older age, non-Asian race, HBV phenotype (inactive CHB vs. others), HBV genotype A, lower HBV-DNA levels, and lower and greater change in qHBsAg. The HBRN-SQuARe (sex,∆quantHBsAg, age, race) score predicted HBsAg loss over time with area under the receiver operating characteristic curve (AUROC) (95% CIs) at 1 and 3 years of 0.99 (95% CI: 0.987-1.00) and 0.95 (95% CI 0.91-1.00), respectively. In validation in another cohort of 1253 HBeAg-negative patients with median follow-up of 3.1 years, HBRN SQuARe predicted HBsAg loss at 1 and 3 years with AUROC values of 0.99 (0.98-1.00) and 0.88 (0.77-0.99), respectively. CONCLUSION: HBsAg loss in predominantly untreated patients with HBeAg-negative CHB can be accurately predicted over a 3-year horizon using a simple validated score (HBRN SQuARe). This prognostication tool can be used to support patient care and counseling.
BACKGROUND AND AIMS: Achieving HBsAg loss is an important landmark in the natural history of chronic hepatitis B (CHB). A more personalized approach to prediction of HBsAg loss is relevant in counseling patients. This study sought to develop and validate a prediction model for HBsAg loss based on quantitative HBsAg levels (qHBsAg) and other baseline characteristics. METHODS: The Hepatitis B Research Network (HBRN) is a prospective cohort including 1240 untreated HBeAg-negative patients (1150 adults, 90 children) with median follow-up of 5.5 years. Incidence rates of HBsAg loss and hepatitis B surface antibody (anti-HBs) acquisition were determined, and a predictor score of HBsAg loss using readily available variables was developed and externally validated. RESULTS: Crude incidence rates of HBsAg loss and anti-HBs acquisition were 1.6 and 1.1 per 100 person-years (PY); 67 achieved sustained HBsAg loss for an incidence rate of 1.2 per 100 PY. Increased HBsAg loss was significantly associated with older age, non-Asian race, HBV phenotype (inactive CHB vs. others), HBV genotype A, lower HBV-DNA levels, and lower and greater change in qHBsAg. The HBRN-SQuARe (sex,∆quantHBsAg, age, race) score predicted HBsAg loss over time with area under the receiver operating characteristic curve (AUROC) (95% CIs) at 1 and 3 years of 0.99 (95% CI: 0.987-1.00) and 0.95 (95% CI 0.91-1.00), respectively. In validation in another cohort of 1253 HBeAg-negative patients with median follow-up of 3.1 years, HBRN SQuARe predicted HBsAg loss at 1 and 3 years with AUROC values of 0.99 (0.98-1.00) and 0.88 (0.77-0.99), respectively. CONCLUSION: HBsAg loss in predominantly untreated patients with HBeAg-negative CHB can be accurately predicted over a 3-year horizon using a simple validated score (HBRN SQuARe). This prognostication tool can be used to support patient care and counseling.
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