超声受控衰减参数联合临床特征评估代谢功能障碍相关脂肪性肝病患者肝纤维化  

Evaluation of liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease using ultrasound controlled attenuation parameter combined with clinical features

作　　者：刘春妤 唐敬宽 赵威 LIU Chunyu;TANG Jingkuan;ZHAO Wei(Department of Ultrasound,Chengdu Xindu Hospital of Traditional Chinese Medicine,Chengdu 610500,China;Department of Clinical Biochemistry,Teaching and Research Office,College of Laboratory Medicine,Chengdu Medical College,Chengdu 610500,China)

机构地区：[1]成都市新都区中医医院超声科,成都610500 [2]成都医学院检验医学院临床生化教研室,成都610500

出　　处：《医学新知》2024年第10期1121-1129,共9页New Medicine

基　　金：四川省科技厅面上项目(2024NSFSC0577)。

摘　　要：目的探讨超声受控衰减参数(controlled attenuation parameter,CAP)结合临床特征构建的预测模型在代谢功能障碍相关脂肪性肝病(metabolic dysfunction-associated steatotic liver disease,MASLD)患者肝纤维化诊断中的价值。方法回顾性纳入了美国国家健康与营养调查(National Health and Nutrition Examination Survey,NHANES)数据库2017-2020年间的MASLD成人样本。根据CAP≥248 dB/m定义MASLD,通过瞬时弹性成像测得肝脏硬度≥8.2 kPa定义肝纤维化,将患者分为纤维化组和非纤维化组。应用Boruta算法筛选特征,联合CAP及临床特征构建预测模型,使用受试者工作特征(receiver operating characteristic,ROC)曲线及曲线下面积(area under curve,AUC)、灵敏度、特异度和准确度评价诊断模型效能。结果共纳入1472例MASLD患者,纤维化组213例,非纤维化组1259例。基于Boruta算法筛选得到腰围、体重指数、CAP、空腹血糖、合并糖尿病、丙氨酸转氨酶、天冬氨酸转氨酶、γ-谷胺酰转移酶、高敏C反应蛋白、年龄、白蛋白、碱性磷酸酶、血清总胆红素和性别14个临床特征。CAP单独预测时,AUC为0.727[95%CI(0.690,0.765)],灵敏度、特异度、准确度分别为62.4%、70.2%、69.1%;CAP联合临床特征预测时,AUC为0.842[95%CI(0.813,0.871)],灵敏度、特异度、准确度分别为75.5%、76.7%、75.6%;Delong检验结果显示两种方法AUC值的差异具有统计学意义(Z=-6.877,P<0.001)。结论CAP结合临床特征构建的预测模型在MASLD纤维化诊断中具有较好的诊断效能,为临床实践提供了有价值的参考工具。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 nonfibrosis 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.

关 键 词：代谢功能障碍相关脂肪性肝病 肝纤维化 超声 受控衰减参数 机器学习 诊断 

分 类 号：R575.5[医药卫生—消化系统]