Comparison of models to predict incident chronic liver disease: a systematic review and external validation in Chinese adults

Abstract

Background: 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).

Methods: 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).

Results: 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.

Conclusions: 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.

Keywords: Chinese; Chronic liver disease; External validation; Hepatocellular carcinoma; Risk prediction; Systematic review.

Fig. 1

Flow chart. A Flow chart for screening eligible publications. B Number of models included in the systematic review and external validation of CKB. The larger box corresponds to the aggregate number of models included in the systematic review, including (1) models originally developed for the target population and (2) those previously validated within the target population (not originally developed for the target populations). The smaller box represents models externally validated in CKB, including 11 models validated in the general population and 2 models validated in HBV infected individuals. Abbreviations: CKB, China Kadoorie Biobank; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease; SRMA, systematic review and meta-analysis

Fig. 2

Calibration plots of 10-year HCC risk prediction models in CKB. Non-lab models and lab models are shown using different colors (blue for non-lab models and red for lab models). Observed to expected (O:E) ratio are shown in lower-right corner of each panel

Fig. 3

Calibration plots of 10-year CLD risk prediction models in CKB. Convention as in Fig. 2

Fig. 4

C-index of risk prediction models for HCC and CLD in meta-analysis of CKB and published studies. Boxes represent the C-index for predicting 10-year A HCC or B CLD in the general population. Diamonds represent summary C-index for each model, with the size of the diamond showing 95% confidence interval. For each model, published studies are sorted according to number of participants. Estimates and 95% CI of the summary C-index are shown in bold. The CIs of the summary estimates for HCC models were truncated because of the relatively low SE calculated using the “metamisc” package