Development and validation of a model to predict incident chronic liver disease in the general population: The CLivD score
Chronic liver disease
Liver disease
DOI:
10.1016/j.jhep.2022.02.021
Publication Date:
2022-03-08T00:54:09Z
AUTHORS (15)
ABSTRACT
Current screening strategies for chronic liver disease focus on detection of subclinical advanced fibrosis but cannot identify those at high future risk severe disease. Our aim was to develop and validate a prediction model incident in the general population based widely available factors.Multivariable Cox regression analyses were used models liver-related outcomes with without laboratory measures (Modellab Modelnon-lab) 25,760 individuals aged 40-70 years. Their data sourced from Finnish population-based health examination surveys FINRISK 1992-2012 Health 2000 (derivation cohort). The externally validated Whitehall II (n = 5,058) Copenhagen City Heart Study (CCHS) 3,049) cohorts.The absolute rate per 100,000 person-years ranged 53 144. final included age, sex, alcohol use (drinks/week), waist-hip ratio, diabetes, smoking, Modellab also gamma-glutamyltransferase values. Internally Wolbers' C-statistics 0.77 0.75 Modelnon-lab, while apparent 15-year AUCs 0.84 (95% CI 0.75-0.93) 0.82 0.74-0.91). identified small proportion (<2%) >10% events. Of all events, only 10% occurred participants lowest category. In validation cohorts, 0.78 (Modellab) 0.65 (Modelnon-lab) CCHS cohort, cohort.Based factors, Chronic Liver Disease (CLivD) score can be predict population.Liver often progresses silently symptoms thus diagnosis is delayed until complications occur prognosis becomes poor. order who have developing future, we developed score, use, or measurement enzyme gamma-glutamyltransferase. CLivD as part counseling, planning further investigations follow-up.
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