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Development and validation of a model to predict incident chronic liver disease in the general population: The CLivD score

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Background & Aims: Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify those at high future risk of severe liver disease. Our aim was to develop and validate a risk prediction model for incident chronic liver disease in the general population based on widely available factors. Methods: Multivariable Cox regression analyses were used to develop prediction models for liver-related outcomes with and without laboratory measures (Modellab and Modelnon-lab) in 25,760 individuals aged 40–70 years. Their data were sourced from the Finnish population-based health examination surveys FINRISK 1992-2012 and Health 2000 (derivation cohort). The models were externally validated in the Whitehall II (n = 5,058) and Copenhagen City Heart Study (CCHS) (n = 3,049) cohorts. Results: The absolute rate of incident liver outcomes per 100,000 person-years ranged from 53 to 144. The final prediction model included age, sex, alcohol use (drinks/week), waist–hip ratio, diabetes, and smoking, and Modellab also included gamma-glutamyltransferase values. Internally validated Wolbers’ C-statistics were 0.77 for Modellab and 0.75 for Modelnon-lab, while apparent 15-year AUCs were 0.84 (95% CI 0.75-0.93) and 0.82 (95% CI 0.74-0.91). The models identified a small proportion (<2%) of the population with >10% absolute 15-year risk for liver events. Of all liver events, only 10% occurred in participants in the lowest risk category. In the validation cohorts, 15-year AUCs were 0.78 (Modellab) and 0.65 (Modelnon-lab) in the CCHS cohort, and 0.78 (Modelnon-lab) in the Whitehall II cohort. Conclusions: Based on widely available risk factors, the Chronic Liver Disease (CLivD) score can be used to predict risk of future advanced liver disease in the general population. Lay summary: Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor. In order to identify individuals in the general population who have a high risk of developing severe liver disease in the future, we developed and validated a Chronic Liver Disease (CLivD) risk prediction score, based on age, sex, alcohol use, waist-hip ratio, diabetes, and smoking, with or without measurement of the liver enzyme gamma-glutamyltransferase. The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up.

OriginalsprogEngelsk
TidsskriftJournal of Hepatology
Vol/bind77
Udgave nummer2
Sider (fra-til)302-311
Antal sider10
ISSN0168-8278
DOI
StatusUdgivet - aug. 2022

Bibliografisk note

Funding Information:
Dr. Åberg was supported by the Mary and Georg Ehrnrooth Foundation , Medicinska Understödsföreningen Liv och Hälsa , Finska Läkaresällskapet , Academy of Finland ( #338544 ), and Sigrid Jusélius Foundation . Dr. Luukkonen was supported by the Novo Nordisk , Sigrid Jusélius , and Instrumentarium Science Foundations . Dr. Salomaa was supported by the Finnish Foundation for Cardiovascular Research . Dr. Nordestgaard was supported by the Danish Heart Foundation and the Research Foundation for the Capital Region of Denmark . The researchers are all independent of the funders. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Funding Information:
Dr. Åberg was supported by the Mary and Georg Ehrnrooth Foundation, Medicinska Understödsföreningen Liv och Hälsa, Finska Läkaresällskapet, Academy of Finland (#338544), and Sigrid Jusélius Foundation. Dr. Luukkonen was supported by the Novo Nordisk, Sigrid Jusélius, and Instrumentarium Science Foundations. Dr. Salomaa was supported by the Finnish Foundation for Cardiovascular Research. Dr. Nordestgaard was supported by the Danish Heart Foundation and the Research Foundation for the Capital Region of Denmark. The researchers are all independent of the funders. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.FINRISK and Health 2000 data used for the research were obtained from THL Biobank. We thank all study participants for their generous participation at THL Biobank and the FINRISK 1992-2012 studies and Health 2000 Survey. We thank all participants in the Whitehall II Study and the Whitehall II researchers and support staff who make the study possible. Whitehall II data are available to qualified researchers for research purposes. Please refer to the Whitehall II data sharing policy at http://www.ucl.ac.uk/whitehallII/data-sharing. The UK Medical Research Council (MR/K013351/1; G0902037), British Heart Foundation (RG/13/2/30098), and US National Institutes of Health (R01HL36310, R01AG013196) have supported collection of data in the Whitehall II Study. We also thank participants and staff at the Copenhagen City Heart Study. We thank Professor Thomas Gerds for his valuable statistical advice.

Publisher Copyright:
© 2022 The Author(s)

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