Twelve-month post-treatment parameters are superior in predicting hepatocellular carcinoma in patients with chronic hepatitis B
Sang Bong Ahn 1 , Jun Choi 2 , Dae Won Jun 3 , Hyunwoo Oh 4 , Eileen L Yoon 5 , Hyoung Su Kim 6 , Soung Won Jeong 7 , Sung Eun Kim 8 , Jae-Jun Shim 9 , Yong Kyun Cho 10 , Hyo Young Lee 1 , Sung Won Han 11 , Mindie H Nguyen 12 , SAINT cohort
Affiliations
Affiliations
1
Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University College of Medicine, Seoul.
2
Department of Fusion Data Analytics, School of Industrial Management Engineering, Korea University, Seoul.
3
Department of Internal Medicine, Hanyang University, College of Medicine, Seoul.
4
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul.
5
Department of Internal Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul.
6
Department of Internal Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul.
7
Department of Internal Medicine, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul.
8
Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si.
9
Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul.
10
Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul.
11
Department of Biostatistical Consulting and Research Lab, School of Medicine, Korea University, Seoul.
12
Division of Gastroenterology and Hepatology, Stanford University Medical Center, Palo Alto, California, USA.
PMID: 33550661 DOI: 10.1111/liv.14820
Abstract
Background & aims: There are currently several prediction models for hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) receiving oral antiviral therapy. However, most models are based on pre-treatment clinical parameters. The current study aimed to develop a novel and practical prediction model for HCC by using both pre- and post-treatment parameters in this population.
Methods: We included two treatment-naïve CHB cohorts who were initiated on oral antiviral therapies: the derivation cohort (n=1,480, Korea prospective SAINT cohort) and the validation cohort (n=426, the US retrospective Stanford Bay cohort). We employed logistic regression, decision tree, lasso regression, support vector machine, and random forest algorithms to develop the HCC prediction model and selected the most optimal method.
Results: We evaluated both pre-treatment and the 12-month clinical parameters on-treatment and found the 12-month on-treatment values to have superior HCC prediction performance. The lasso logistic regression algorithm using the presence of cirrhosis at baseline and alpha-fetoprotein and platelet at 12 months showed the best performance (AUROC=0.843 in the derivation cohort. The model performed well in the external validation cohort (AUROC=0.844) and better than other existing prediction models including the APA, PAGE-B, and GAG models (AUROC=0.769 to 0.818).
Conclusions: We provided a simple-to-use HCC prediction model based on presence of cirrhosis at baseline and two objective laboratory markers (AFP and platelets) measured 12 months after antiviral initiation. The model is highly accurate with excellent validation in an external cohort from a different country (AUROC 0.844). (Clinical trial number: KCT0003487).
This article is protected by copyright. All rights reserved. 作者: StephenW 时间: 2021-2-9 19:34
治疗后十二个月的参数在预测慢性乙型肝炎患者的肝细胞癌方面具有优越性
Sang Bong Ahn 1，Jun Choi 2，Dae Won Jun 3，Hyunwoo Oh 4，Eileen L Yoon 5，Hyoung Su Kim 6，Soung Won Jeong 7，Sung Eun Kim 8，Jae-Jun Shim 9，Yong Kyun Cho 10，Hyo李英1，宋元翰11，敏迪·阮12，圣马丁
隶属关系
隶属关系