BACKGROUND & AIMS: Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify persons at high future risk for 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 = 5058) and Copenhagen City Heart Study (CCHS) (n = 3049) 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 (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, this Chronic Liver Disease (CLivD) score can be used to predict risk for 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 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, smoking, with or without gamma-glutamyltransferase (GGT). The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up
