Background and aims Steatotic liver disease(SLD)is a growing global concern.The Chronic Liver Disease(CLivD)risk score predicts liver-related outcomes in the general population using easily accessible variables with o...Background and aims Steatotic liver disease(SLD)is a growing global concern.The Chronic Liver Disease(CLivD)risk score predicts liver-related outcomes in the general population using easily accessible variables with or without laboratory tests(CLivDlab and CLivDnon-lab).We assessed CLivD’s associations with liver steatosis,fibrosis and its combined performance with fibrosis-4(FIB-4)for advanced fibrosis detection.Methods Using the National Health and Nutrition Examination Survey data(2017–2020),3603 participants aged 40–70 years with valid liver stiffness measurements(LSMs)were included.Advanced fibrosis was defined as LSM≥12 kPa,and SLD as controlled attenuation parameter≥288 dB/m.Results Significant associations were found between CLivD and SLD and advanced fibrosis.CLivDlab had an area under the curve(AUC)for advanced fibrosis of 0.72(95%CI 0.68 to 0.77),while CLivDnon-lab had an AUC of 0.68(95%CI 0.64 to 0.72),both slightly higher than FIB-4(AUC 0.66,95%CI 0.60 to 0.72).Among participants without obesity,AUC of CLivDlab was 0.82(95%CI 0.76 to 0.88)and AUC of CLivDnon-lab was 0.72(95%CI 0.65 to 0.79).The CLivD score improved FIB-4’s AUC for advanced fibrosis detection from<0.5 at minimal CLivD scores to>0.8 at high CLivD scores.A sequential CLivD→FIB-4 strategy outperformed universal FIB-4 testing,enhancing specificity from 72%to 83%,with sensitivity at 51%–53%.This strategy identified a subgroup with a 55%prevalence of advanced fibrosis,while 47%had minimal-risk CLivD scores,eliminating the need for FIB-4 testing.Conclusions The CLivD score,designed for predicting liver-related outcomes,effectively identifies liver steatosis and advanced fibrosis in the general population.Combining CLivD with FIB-4 enhances advanced fibrosis detection accuracy.The CLivD score could enhance population-based liver fibrosis screening,optimising resource allocation.展开更多
Liver disease poses a significant global health burden,with steatotic liver disease related to metabolic dysfunction and/or alcohol use being the most prevalent type.Current risk stratification strategies emphasize de...Liver disease poses a significant global health burden,with steatotic liver disease related to metabolic dysfunction and/or alcohol use being the most prevalent type.Current risk stratification strategies emphasize detecting advanced fibrosis as a surrogate marker for liver-related events(LREs),such as hospitalization,liver cancer,or death.However,fibrosis alone does not adequately predict imminent outcomes,particularly in fast-progressing individuals without advanced fibrosis at evaluation.This underscores the need for models designed specifically to predict LREs,enabling timely interventions.The Chronic Liver Disease(CLivD)risk score,the dynamic aspartate aminotransferase-to-alanine aminotransferase ratio(dAAR),and the Cirrhosis Outcome Risk Estimator(CORE)were explicitly developed to predict LRE risk rather than detect fibrosis.Derived from general population cohorts,these models incorporate either standard liver enzymes(dAAR and CORE)or risk factors(CLivD),enabling broad application in primary care and populationbased settings.They directly estimate the risk of future LREs,improving on traditional fibrosis-focused approaches.Conversely,widely used models like the Fibrosis-4 index and newer ones,such as the LiverRisk and LiverPRO scores,were initially developed to detect significant/advanced fibrosis or liver stiffness.While not designed for LRE prediction,they have later been analyzed for this purpose.Integrating fibrosis screening with LRE-focused models like CLivD,dAAR,and CORE can help healthcare systems adopt proactive,preventive care.This approach emphasizes identifying individuals at imminent risk of severe outcomes,potentially ensuring better resource allocation and personalized interventions.展开更多
基金F.was supported by Finska L.kares.llskapet,Academy of Finland(#338544),Sigrid Jusélius Foundation,Mary and Georg Ehrnrooth Foundation,Medicinska Underst.dsf.rening Liv&H.lsaWilhelm and Else Stockmann Foundation.ML was supported by Finnish State Research Funding and Mary and Georg Ehrnrooth Foundation.The researchers are independent of the funders.
文摘Background and aims Steatotic liver disease(SLD)is a growing global concern.The Chronic Liver Disease(CLivD)risk score predicts liver-related outcomes in the general population using easily accessible variables with or without laboratory tests(CLivDlab and CLivDnon-lab).We assessed CLivD’s associations with liver steatosis,fibrosis and its combined performance with fibrosis-4(FIB-4)for advanced fibrosis detection.Methods Using the National Health and Nutrition Examination Survey data(2017–2020),3603 participants aged 40–70 years with valid liver stiffness measurements(LSMs)were included.Advanced fibrosis was defined as LSM≥12 kPa,and SLD as controlled attenuation parameter≥288 dB/m.Results Significant associations were found between CLivD and SLD and advanced fibrosis.CLivDlab had an area under the curve(AUC)for advanced fibrosis of 0.72(95%CI 0.68 to 0.77),while CLivDnon-lab had an AUC of 0.68(95%CI 0.64 to 0.72),both slightly higher than FIB-4(AUC 0.66,95%CI 0.60 to 0.72).Among participants without obesity,AUC of CLivDlab was 0.82(95%CI 0.76 to 0.88)and AUC of CLivDnon-lab was 0.72(95%CI 0.65 to 0.79).The CLivD score improved FIB-4’s AUC for advanced fibrosis detection from<0.5 at minimal CLivD scores to>0.8 at high CLivD scores.A sequential CLivD→FIB-4 strategy outperformed universal FIB-4 testing,enhancing specificity from 72%to 83%,with sensitivity at 51%–53%.This strategy identified a subgroup with a 55%prevalence of advanced fibrosis,while 47%had minimal-risk CLivD scores,eliminating the need for FIB-4 testing.Conclusions The CLivD score,designed for predicting liver-related outcomes,effectively identifies liver steatosis and advanced fibrosis in the general population.Combining CLivD with FIB-4 enhances advanced fibrosis detection accuracy.The CLivD score could enhance population-based liver fibrosis screening,optimising resource allocation.
文摘Liver disease poses a significant global health burden,with steatotic liver disease related to metabolic dysfunction and/or alcohol use being the most prevalent type.Current risk stratification strategies emphasize detecting advanced fibrosis as a surrogate marker for liver-related events(LREs),such as hospitalization,liver cancer,or death.However,fibrosis alone does not adequately predict imminent outcomes,particularly in fast-progressing individuals without advanced fibrosis at evaluation.This underscores the need for models designed specifically to predict LREs,enabling timely interventions.The Chronic Liver Disease(CLivD)risk score,the dynamic aspartate aminotransferase-to-alanine aminotransferase ratio(dAAR),and the Cirrhosis Outcome Risk Estimator(CORE)were explicitly developed to predict LRE risk rather than detect fibrosis.Derived from general population cohorts,these models incorporate either standard liver enzymes(dAAR and CORE)or risk factors(CLivD),enabling broad application in primary care and populationbased settings.They directly estimate the risk of future LREs,improving on traditional fibrosis-focused approaches.Conversely,widely used models like the Fibrosis-4 index and newer ones,such as the LiverRisk and LiverPRO scores,were initially developed to detect significant/advanced fibrosis or liver stiffness.While not designed for LRE prediction,they have later been analyzed for this purpose.Integrating fibrosis screening with LRE-focused models like CLivD,dAAR,and CORE can help healthcare systems adopt proactive,preventive care.This approach emphasizes identifying individuals at imminent risk of severe outcomes,potentially ensuring better resource allocation and personalized interventions.
