Ma Xinyan, Huang Lili, Yu Meijie, Dong Rui, Wang Yifan, Chen Hongbo, Yu Rongbin, Huang Peng, Wang Jie
Department of Epidemiology, Center for Global Health, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, China.
NHC Key Laboratory of Contraceptives Vigilance and Fertility Surveillance/Jiangsu Health Development Research Center, Nanjing, China.
Cancer Control. 2025 Jan-Dec;32:10732748251316609. doi: 10.1177/10732748251316609.
The aim of this study was to develop and internally validate a hepatocellular carcinoma (HCC) risk prediction model incorporating repeated-measures data (longitudinal model), and compare with baseline predictions.
A total of 1097 participants with chronic hepatitis C after direct-acting antivirals (DAA) treatment were included in this prospective cohort study. The framework of joint models for longitudinal and survival data was used to construct the longitudinal prediction model. For comparison, a baseline model incorporating the same predictors was constructed through the multivariate Cox regression models. Model performance was evaluated using dynamic discrimination index (DDI), areas under the receiver-operating characteristics curves (AUROC), and Brier scores.
Over a median follow-up of 7.25 years, 60 patients (5.5%) developed HCC. Key risk factors identified were aspartate aminotransferase (AST), cholinesterase, gamma-glutamyl transferase (GGT), albumin, hemoglobin (Hb), platelet count, alpha-fetoprotein (AFP), antigen-125 (CA-125), and carcinoembryonic antigen (CEA). The final joint model, with GGT and CEA removed, showed superior average predictive performance (DDI = .871) compared to models with all predictors included. Validation showed high predictive accuracy for HCC, with AUROCs above .9 for 1-, 3-, 4-, and 5-year predictions. In comparison, the baseline Cox model only achieved mediocre AUROCs of .7 (.75, .67, .69, and .67, respectively).
Compared to static models, our dynamic prediction model can predict the risk of HCC in patients after DAA treatment more accurately, providing better information to distinguish high-risk populations.
本研究旨在开发并内部验证一个纳入重复测量数据的肝细胞癌（HCC）风险预测模型（纵向模型），并与基线预测进行比较。
本前瞻性队列研究纳入了1097例接受直接抗病毒药物（DAA）治疗后的慢性丙型肝炎患者。采用纵向和生存数据联合模型框架构建纵向预测模型。为作比较，通过多变量Cox回归模型构建了一个纳入相同预测因子的基线模型。使用动态鉴别指数（DDI）、受试者操作特征曲线下面积（AUROC）和Brier评分评估模型性能。
在中位随访7.25年期间，60例患者（5.5%）发生了HCC。确定的关键风险因素包括天冬氨酸转氨酶（AST）、胆碱酯酶、γ-谷氨酰转移酶（GGT）、白蛋白、血红蛋白（Hb）、血小板计数、甲胎蛋白（AFP）、癌抗原125（CA-125）和癌胚抗原（CEA）。最终的联合模型去除了GGT和CEA，与纳入所有预测因子的模型相比，显示出更好的平均预测性能（DDI = 0.871）。验证显示该模型对HCC具有较高的预测准确性，1年、3年、4年和5年预测的AUROC均高于0.9。相比之下，基线Cox模型的AUROC仅为中等水平，分别为0.7（0.75、0.67、0.69和0.67）。
与静态模型相比，我们的动态预测模型能够更准确地预测DAA治疗后患者发生HCC的风险，为区分高危人群提供更好的信息。
