Department of Hepatology, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China.
Department of Endocrinology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Clin Chim Acta. 2017 Dec;475:44-50. doi: 10.1016/j.cca.2017.09.020. Epub 2017 Sep 28.
Several non-invasive diagnostic scores for non-alcoholic fatty liver (NAFL) have been developed, but the clinical application is limited because of their complexity.
To develop and validate an easy-to-calculate scoring system to identify ultrasound-diagnosed NAFL.
48,489 patients from 2 centers were included in this study. Multivariable logistic regression models were employed for model development. Ultrasonography was applied to diagnose NAFL. The selected variables were assigned an integer score proportional to the estimated coefficient from the logistic regression analysis, namely NAFL Screening Score (NSS). The ability of the NSS to identify NAFL was assessed by analyzing the area under the receiver operating characteristic curve (AUROC) and was tested in an independent validation cohort. Additionally, the performance of NSS was compared with existing models.
NSS was developed as a basic score comprising of age, body mass index (BMI), triglyceride (TG), ALT/AST, fasting plasma glucose (FPG) and uric acid (UA) in both sexes. NSS showed a relatively good discriminative power (AUROC=0.825 for males, 0.861 for females in the validation cohort) in comparison with other models. The optimal cut-off point was 32 for males and 29 for females.
We developed and validated NSS, an easy-to-use score sheet identify ultrasound-diagnosed NAFL. NSS may be clinically useful for initial diagnosing NAFL.
已经开发出了几种用于非酒精性脂肪肝 (NAFL) 的非侵入性诊断评分,但由于其复杂性,临床应用受到限制。
开发和验证一种易于计算的评分系统,以识别超声诊断的 NAFL。
本研究纳入了来自 2 个中心的 48489 名患者。采用多变量逻辑回归模型进行模型开发。超声用于诊断 NAFL。选择的变量被分配一个整数分数,与逻辑回归分析的估计系数成正比,即 NAFL 筛查评分 (NSS)。通过分析接收者操作特征曲线 (AUROC) 下的面积来评估 NSS 识别 NAFL 的能力,并在独立验证队列中进行测试。此外,还比较了 NSS 的性能与现有模型。
NSS 是作为一个基本评分开发的,包括男女两性的年龄、体重指数 (BMI)、三酰甘油 (TG)、丙氨酸氨基转移酶 (ALT)/天冬氨酸氨基转移酶 (AST)、空腹血糖 (FPG) 和尿酸 (UA)。NSS 在区分能力方面表现相对较好(AUROC 男性为 0.825,女性为 0.861,在验证队列中),与其他模型相比。最佳截断值为男性 32 分,女性 29 分。
我们开发并验证了 NSS,这是一种用于识别超声诊断的 NAFL 的简单易用的评分表。NSS 可能对 NAFL 的初步诊断具有临床应用价值。