한빛사논문
- 조회97
Moon Haeng Hur 1∗, Terry Cheuk-Fung Yip 2∗, Seung Up Kim 3∗, Hyun Woong Lee 3∗, Han Ah Lee 4∗, Hyung-Chul Lee 5∗, Grace Lai-Hung Wong 2, Vincent Wai-Sun Wong 2, Jun Yong Park 3, Sang Hoon Ahn 3, Beom Kyung Kim 3, Hwi Young Kim 4, Yeon Seok Seo 6, Hyunjae Shin 1, Jeayeon Park 1, Yunmi Ko 1, Youngsu Park 1, Yun Bin Lee 1, Su Jong Yu 1, Sang Hyub Lee 1, Yoon Jun Kim 1, Jung-Hwan Yoon 1, Jeong-Hoon Lee 1
1Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
2Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
3Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
4Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea
5Department of Anesthesiology, Seoul National University College of Medicine, Seoul, Korea
6Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea
∗These authors equally contributed to this study as co-first authors.
Corresponding Author: Jeong-Hoon Lee
Abstract
Background & aims: The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-related outcomes (LROs) following HBsAg seroclearance.
Methods: A total of 4,787 consecutive patients who achieved HBsAg seroclearance between 2000 and 2022 were enrolled from 6 centers in South Korea and a territory-wide database in Hong Kong, comprising the training (n=944), internal validation (n=1,102), and external validation (n=2,741) cohorts. Three machine learning-based models were developed and compared in each cohort. The primary outcome was the development of any LRO, including HCC, decompensation, and liver-related death.
Results: During a median follow-up of 55.2 (interquartile range=30.1-92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. A model with the best predictive performance in the training cohort was selected as the final model (designated as PLAN-B-CURE), which was constructed using a gradient boosting algorithm and 7 variables (age, sex, diabetes, alcohol consumption, cirrhosis, albumin, and platelet count). Compared to previous HCC prediction models, PLAN-B-CURE showed significantly superior accuracy in the training cohort (c-index: 0.82 vs. 0.63-0.70, all P<0.001; area under the receiver operating characteristic curve: 0.86 vs. 0.62-0.72, all P<0.01; area under the precision-recall curve: 0.53 vs. 0.13-0.29, all P<0.01). PLAN-B-CURE showed a reliable calibration function (Hosmer-Lemeshow test P>0.05) and these results were reproduced in the internal and external validation cohorts.
Conclusion: This novel machine learning model consisting of 7 variables provides reliable risk prediction of LRO after HBsAg seroclearance that can be used for personalized surveillance.
논문정보
- Research article
- 2024년 08월 (BRIC 등록일 2024-09-03)
- 국내(교신)+국외 연구진
- 의약학 > 응용의학
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
