Evaluation of liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease using ultrasound controlled attenuation parameter combined with clinical features
LIU Chunyu
TANG Jingkuan
ZHAO Wei
Abstract：Objective To explore the value of constructing a predictive model using ultrasound controlled attenuation parameter(CAP)combined with clinical features in diagnosing fibrosis in patients with metabolic dysfunction-associated steatotic liver disease(MASLD).Methods This retrospective study analyzed adult samples from the National Health and Nutrition Examination Survey(NHANES)database between 2017 and 2020.MASLD was defined as CAP ≥ 248 dB/m,and fibrosis was defined as liver stiffness measured by transient elastography ≥ 8.2 kPa.Patients were divided into fibrosis and non-fibrosis groups.Features were selected using the Boruta algorithm,and a predictive model combining CAP and clinical features was constructed.The receiver operating characteristic curve and area under curve(AUC),sensitivity,specificity and accuracy were used to evaluate the model.Results A total of 1,472 MASLD patients were identified,with 213 patients in the fibrosis group and 1,259 in the non-fibrosis group.The features screened by the Boruta algorithm included waist circumference,body mass index,CAP,blood glucose,combined diabetes,ALT,AST,GGT,hs-CRP,age,ALB,ALP,STB and gender.AUC for CAP alone in predicting liver fibrosis was 0.727[95％CI(0.690,0.765)]with a sensitivity of 62.4％,specificity of 70.2％,and accuracy of 69.1％.The AUC increased to 0.842[95％(0.813,0.871)]when combining CAP with clinical features,with a sensitivity of 75.5％,specificity of 76.7％,and accuracy of 75.6％.Delong's test comparing the AUC values of CAP alone and CAP combined with clinical indicators indicated a statistically significant difference(Z=-6.877,P＜0.001).Conclusion The prediction model constructed by CAP in combination with clinical features has good diagnostic efficacy in the diagnosis of MASLD fibrosis and provides a valuable reference tool for clinical practice.
Keywords：Metabolic dysfunction-associated steatotic liver diseaseLiver fibrosisUltrasoundControlled attenuation parameterMachine learningDiagnose
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 1121-1129 )