Hyun-Seok Kim1, Xian Yu2, Jennifer Kramer2, Aaron P Thrift3, Pete Richardson2, Yao-Chun Hsu4, Avegail Flores5, Hashem B El-Serag6, Fasiha Kanwal7. 1. Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA. 2. Clinical Epidemiology and Comparative Effectiveness Program, Section of Health Services Research (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA. 3. Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA. 4. Center for Liver Diseases and Division of Gastroenterology and Hepatology, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan. 5. Clinical Epidemiology and Comparative Effectiveness Program, Section of Health Services Research (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA; Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA. 6. Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA; Clinical Epidemiology and Comparative Effectiveness Program, Section of Health Services Research (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA. 7. Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA; Clinical Epidemiology and Comparative Effectiveness Program, Section of Health Services Research (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA. Electronic address: kanwal@bcm.edu.
Abstract
BACKGROUND & AIMS: Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. METHODS: Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. RESULTS: Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. CONCLUSION: In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. LAY SUMMARY: Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.
BACKGROUND & AIMS: Guidelines recommend hepatocellular carcinoma (HCC) surveillance in patients with chronic HBV infection. Several HCC risk prediction models are available to guide surveillance decisions, but their comparative performance remains unclear. METHODS: Using a retrospective cohort of patients with HBV treated with nucleos(t)ide analogues at 130 Veterans Administration facilities between 9/1/2008 and 12/31/2018, we calculated risk scores from 10 HCC risk prediction models (REACH-B, PAGE-B, m-PAGE-B, CU-HCC, HCC-RESCUE, CAMD, APA-B, REAL-B, AASL-HCC, RWS-HCC). We estimated the models' discrimination and calibration. We calculated HCC incidence in risk categories defined by the reported cut-offs for all models. RESULTS: Of 3,101 patients with HBV (32.2% with cirrhosis), 47.0% were treated with entecavir, 40.6% tenofovir, and 12.4% received both. During a median follow-up of 4.5 years, 113 patients developed HCC at an incidence of 0.75/100 person-years. AUC values for 3-year HCC risk were the highest for RWS-HCC, APA-B, REAL-B, and AASL-HCC (all >0.80). Of these, 3 (APA-B, RWS-HCC, REAL-B) incorporated alpha-fetoprotein. AUC values for the other models ranged from 0.73 for PAGE-B to 0.79 for CAMD and HCC-RESCUE. Of the 7 models with AUC >0.75, only APA-B was poorly calibrated. In total, 10-20% of the cohort was deemed low-risk based on the published cut-offs. None of the patients in the low-risk groups defined by PAGE-B, m-PAGE-B, AASL-HCC, and REAL-B developed HCC during the study timeframe. CONCLUSION: In this national cohort of US-based patients with HBV on antiviral treatment, most models performed well in predicting HCC risk. A low-risk group, in which no cases of HCC occurred within a 3-year timeframe, was identified by several models (PAGE-B, m-PAGE-B, CAMD, AASL-HCC, REAL-B). Further studies are warranted to examine whether these patients could be excluded from HCC surveillance. LAY SUMMARY: Risk prediction models for hepatocellular carcinoma (HCC) in patients infected with hepatitis B virus (HBV) could guide HCC surveillance decisions. In this large cohort of US-based patients receiving treatment for HBV, most published models discriminated between those who did or did not develop HCC, although the RWS-HCC, REAL-B, and AASL-HCC performed the best. If confirmed in future studies, these models could help identify a low-risk subset of patients on antiviral treatment who could be excluded from HCC surveillance.
Authors: Nabihah Tayob; Israel Christie; Peter Richardson; Ziding Feng; Donna L White; Jessica Davila; Douglas A Corley; Fasiha Kanwal; Hashem B El-Serag Journal: Clin Gastroenterol Hepatol Date: 2018-12-14 Impact factor: 11.382
Authors: J R Kramer; J A Davila; E D Miller; P Richardson; T P Giordano; H B El-Serag Journal: Aliment Pharmacol Ther Date: 2007-11-08 Impact factor: 8.171
Authors: Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan Journal: Epidemiology Date: 2010-01 Impact factor: 4.822
Authors: George Papatheodoridis; George Dalekos; Vana Sypsa; Cihan Yurdaydin; Maria Buti; John Goulis; Jose Luis Calleja; Heng Chi; Spilios Manolakopoulos; Giampaolo Mangia; Nikolaos Gatselis; Onur Keskin; Savvoula Savvidou; Juan de la Revilla; Bettina E Hansen; Ioannis Vlachogiannakos; Kostantinos Galanis; Ramazan Idilman; Massimo Colombo; Rafael Esteban; Harry L A Janssen; Pietro Lampertico Journal: J Hepatol Date: 2015-12-08 Impact factor: 25.083