Cong Xue, Song Shuyao, Li Yingtao, Song Kaiyang, MacLeod Cameron, Cheng Yujie, Lv Jun, Yu Canqing, Sun Dianjianyi, Pei Pei, Yang Ling, Chen Yiping, Millwood Iona, Wu Shukuan, Yang Xiaoming, Stevens Rebecca, Chen Junshi, Chen Zhengming, Li Liming, Kartsonaki Christiana, Pang Yuanjie
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Medical Sciences Division, University of Oxford, Oxford, OX3 9DU, UK.
BMC Med. 2024 Dec 31;22(1):601. doi: 10.1186/s12916-024-03754-9.
Risk prediction models can identify individuals at high risk of chronic liver disease (CLD), but there is limited evidence on the performance of various models in diverse populations. We aimed to systematically review CLD prediction models, meta-analyze their performance, and externally validate them in 0.5 million Chinese adults in the China Kadoorie Biobank (CKB).
Models were identified through a systematic review and categorized by the target population and outcomes (hepatocellular carcinoma [HCC] and CLD). The performance of models to predict 10-year risk of CLD was assessed by discrimination (C-index) and calibration (observed vs predicted probabilies).
The systematic review identified 57 articles and 114 models (28.4% undergone external validation), including 13 eligible for validation in CKB. Models with high discrimination (C-index ≥ 0.70) in CKB were as follows: (1) general population: Li-2018 and Wen 1-2012 for HCC, CLivD score (non-lab and lab) and dAAR for CLD; (2) hepatitis B virus (HBV) infected individuals: Cao-2021 for HCC and CAP-B for CLD. In CKB, all models tended to overestimate the risk (O:E ratio 0.55-0.94). In meta-analysis, we further identified models with high discrimination: (1) general population (C-index ≥ 0.70): Sinn-2020, Wen 2-2012, and Wen 3-2012 for HCC, and FIB-4 and Forns for CLD; (2) HBV infected individuals (C-index ≥ 0.80): RWS-HCC and REACH-B IIa for HCC and GAG-HCC for HCC and CLD.
Several models showed good discrimination and calibration in external validation, indicating their potential feasibility for risk stratification in population-based screening programs for CLD in Chinese adults.
风险预测模型可识别慢性肝病（CLD）高风险个体，但关于各种模型在不同人群中性能的证据有限。我们旨在系统评价CLD预测模型，对其性能进行荟萃分析，并在中国嘉道理生物银行（CKB）的50万中国成年人中对其进行外部验证。
通过系统评价识别模型，并按目标人群和结局（肝细胞癌[HCC]和CLD）进行分类。通过区分度（C指数）和校准（观察概率与预测概率）评估模型预测CLD 10年风险的性能。
系统评价共识别出57篇文章和114个模型（28.4%经过外部验证），其中13个符合在CKB中进行验证的条件。在CKB中具有高区分度（C指数≥0.70）的模型如下：（1）一般人群：用于HCC的Li - 2018和Wen 1 - 2012，用于CLD的CLivD评分（非实验室和实验室）和dAAR；（2）乙型肝炎病毒（HBV）感染个体：用于HCC的Cao - 2021和用于CLD的CAP - B。在CKB中，所有模型均倾向于高估风险（观察值与预期值之比为0.55 - 0.94）。在荟萃分析中，我们进一步识别出具有高区分度的模型：（1）一般人群（C指数≥0.70）：用于HCC的Sinn - 2020、Wen 2 - 2012和Wen 3 - 2012，用于CLD的FIB - 4和Forns；（2）HBV感染个体（C指数≥0.80）：用于HCC的RWS - HCC和REACH - B IIa，以及用于HCC和CLD的GAG - HCC。
一些模型在外部验证中显示出良好的区分度和校准，表明它们在基于人群的中国成年人CLD筛查计划中进行风险分层具有潜在可行性。
