TY - JOUR
T1 - A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B
AU - Hur, Moon Haeng
AU - Yip, Terry Cheuk Fung
AU - Kim, Seung Up
AU - Lee, Hyun Woong
AU - Lee, Han Ah
AU - Lee, Hyung Chul
AU - Wong, Grace Lai Hung
AU - Wong, Vincent Wai Sun
AU - Park, Jun Yong
AU - Ahn, Sang Hoon
AU - Kim, Beom Kyung
AU - Kim, Hwi Young
AU - Seo, Yeon Seok
AU - Shin, Hyunjae
AU - Park, Jeayeon
AU - Ko, Yunmi
AU - Park, Youngsu
AU - Lee, Yun Bin
AU - Yu, Su Jong
AU - Lee, Sang Hyub
AU - Kim, Yoon Jun
AU - Yoon, Jung Hwan
AU - Lee, Jeong Hoon
N1 - Publisher Copyright:
© 2024 European Association for the Study of the Liver
PY - 2025/2
Y1 - 2025/2
N2 - 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 six 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 (IQR 30.1–92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. The 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 seven 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 seven variables provides reliable risk prediction of LROs after HBsAg seroclearance that can be used for personalized surveillance. Impact and implications: Using large-scale multinational data, we developed a machine learning model to predict the risk of liver-related outcomes (i.e., hepatocellular carcinoma, decompensation, and liver-related death) after the functional cure of chronic hepatitis B (CHB). The new model named PLAN-B-CURE was constructed using seven variables (age, sex, alcohol consumption, diabetes, cirrhosis, serum albumin, and platelet count) and a gradient boosting machine algorithm, and it demonstrated significantly better predictive accuracy than previous models in both the training and validation cohorts. The inclusion of diabetes and significant alcohol intake as model inputs suggests the importance of metabolic risk factor management after the functional cure of CHB. Using seven readily available clinical factors, PLAN-B-CURE, the first machine learning-based model for risk prediction after the functional cure of CHB, may serve as a basis for individualized risk stratification.
AB - 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 six 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 (IQR 30.1–92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. The 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 seven 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 seven variables provides reliable risk prediction of LROs after HBsAg seroclearance that can be used for personalized surveillance. Impact and implications: Using large-scale multinational data, we developed a machine learning model to predict the risk of liver-related outcomes (i.e., hepatocellular carcinoma, decompensation, and liver-related death) after the functional cure of chronic hepatitis B (CHB). The new model named PLAN-B-CURE was constructed using seven variables (age, sex, alcohol consumption, diabetes, cirrhosis, serum albumin, and platelet count) and a gradient boosting machine algorithm, and it demonstrated significantly better predictive accuracy than previous models in both the training and validation cohorts. The inclusion of diabetes and significant alcohol intake as model inputs suggests the importance of metabolic risk factor management after the functional cure of CHB. Using seven readily available clinical factors, PLAN-B-CURE, the first machine learning-based model for risk prediction after the functional cure of CHB, may serve as a basis for individualized risk stratification.
KW - artificial intelligence
KW - decompensation
KW - liver cancer
KW - seroclearance
KW - surface antigen
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U2 - 10.1016/j.jhep.2024.08.016
DO - 10.1016/j.jhep.2024.08.016
M3 - Article
C2 - 39218223
AN - SCOPUS:85206101938
SN - 0168-8278
VL - 82
SP - 235
EP - 244
JO - Journal of Hepatology
JF - Journal of Hepatology
IS - 2
ER -