使用纵向临床数据动态预测慢性乙型肝炎患者肝硬化风险

StephenW

Dynamic prediction of liver cirrhosis risk in chronic hepatitis B patients using longitudinal clinical data

Wang, Yinga,,*; Li, Xiang-Yongb,,*; Wu, Li-Lic,,*; Zheng, Xiao-Yanc; Deng, Yua; Li, Meng-Jiea; You, Xud; Chong, Yu-Tianb; Hao, Yuan-Taoa
European Journal of Gastroenterology & Hepatology: January 2020 - Volume 32 - Issue 1 - p 120–126
doi: 10.1097/MEG.0000000000001592
Original Articles: Hepatology
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Objectives: In longitudinal studies, serum biomarkers are often measured longitudinally which is valuable to predict the risk of disease progression. Previous risk prediction models for liver cirrhosis restrict data to baseline or baseline and a single follow-up time point, which failed to incorporate the time-dependent marker information. The aim of this study is to develop risk model in patients with chronic hepatitis B for dynamic prediction of cirrhosis by incorporating longitudinal clinical data.

Methods: Data from the hospital-based retrospective cohort at the Third Affiliated Hospital of Sun Yat-sen University, from 2004 to 2016, were analyzed. Using the multilevel logistic regression model, the time-dependent marker information and individual characteristics were taken as input, and the risk of at different time as the output.

Results: At the end of follow-up, 8.8% of patients progressed to cirrhosis, the average estimate values of hepatitis B virus DNA and alanine aminotransferase demonstrated a downward trend, the aspartate aminotransferase/alanine aminotransferase ratio showed a flat trend overall. The important predictors were as follows: age, oral antiviral treatment, hepatitis B virus DNA. This risk prediction model had an area under the receiver operator characteristic curve of 0.835 (95% confidence interval: 0.772–0.899) and 0.809 (95% confidence interval: 0.708–0.910) in the derivation and validation sets, respectively.

Conclusion: Longitudinal prediction model can be used for dynamic prediction of disease progression and identify changing high-risk patients.

StephenW

使用纵向临床数据动态预测慢性乙型肝炎患者肝硬化风险

王英加，*；李向勇，*；吴丽丽，*;郑小燕；邓瑜李梦洁你，许德；冲宇天郝元陶
《欧洲胃肠病学和肝病学杂志》：2020年1月-第32卷-第1期-第120–126页
doi：10.1097 / MEG.0000000000001592
原始文章：肝病学
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目的：在纵向研究中，血清生物标志物通常是纵向测量的，对于预测疾病进展的风险非常有价值。以前的肝硬化风险预测模型将数据限制在基线或基线以及单个随访时间点上，而这些时间点未能纳入与时间相关的标记信息。这项研究的目的是通过合并纵向临床数据，为慢性乙型肝炎患者建立动态预测肝硬化的风险模型。

方法：分析中山大学附属第三医院2004年至2016年的回顾性研究数据。使用多级逻辑回归模型，将时间相关标记信息和个体特征作为输入，将不同时间的风险作为输出。

结果：随访结束时，有8.8％的患者发展为肝硬化，乙型肝炎病毒DNA和丙氨酸氨基转移酶的平均估计值呈下降趋势，天冬氨酸氨基转移酶/丙氨酸氨基转移酶之比总体呈持平趋势。重要的预测指标如下：年龄，口服抗病毒治疗，乙型肝炎病毒DNA。在衍生和验证集中，该风险预测模型在接收者操作员特征曲线下的面积分别为0.835（95％置信区间：0.772-0.899）和0.809（95％置信区间：0.708-0.910）。

结论：纵向预测模型可用于动态预测疾病进展并识别变化的高危患者。