In:
European Journal of Gastroenterology & Hepatology, Ovid Technologies (Wolters Kluwer Health), Vol. 31, No. 7 ( 2019-07), p. 865-872
Kurzfassung:
Accurate assessment of hepatocellular carcinoma (HCC) risk in chronic hepatitis B (CHB) patients receiving entecavir (ETV)/tenofovir disoproxil fumarate (TDF) is likely to play a pivotal role in post-treatment follow-up strategy. We aimed to develop a simple and reliable predictive model for HCC risk in these patients. Patients and methods A database of 1242 consecutive treatment-naive CHB patients who initially underwent ETV/TDF between February 2007 and January 2017 at four referral hospitals in South Korea was analyzed. The HCC risk model was constructed on the basis of a multivariable Cox proportional hazards model in the derivation dataset ( n =944) and was validated using Harrell’s C -statistic in a validation dataset ( n =298). Results The 3/5-year cumulative incidence rates of HCC were 3.9/6.5 and 4.2/11.6% in the derivation and the validation dataset, respectively ( P =0.08). In the derivation dataset, we identified four factors associated with HCC, namely, age, albumin, sex, and liver cirrhosis. The AASL (age, albumin, sex, liver cirrhosis)-HCC scoring system was developed on the basis of these factors, and simplified to an integer scoring system. AASL-HCC scores were found to have high discriminating performance for the prediction of HCC development at 5 years in the derivation ( C -statistics=0.802, 95% confidence interval: 0.716–0.888) and validation dataset ( C -statistics=0.805, 95% confidence interval: 0.671–0.939). When AASL-HCC scores were classified as 5 or less, 6–19, and at least 20 (low-risk, intermediate-risk, and high-risk groups, respectively), the 5-year cumulative incidence rates of HCC were 0, 4.2, and 17.6%, respectively, in the derivation dataset. Conclusions The AASL-HCC model was simple and reliable for HCC risk prediction in treatment-naive CHB patients receiving ETV/TDF, and is easily applicable in the clinical setting.
Materialart:
Online-Ressource
ISSN:
0954-691X
DOI:
10.1097/MEG.0000000000001357
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2019
ZDB Id:
2030291-5
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