Yu-Jie Zhou1, Yi-Fan Zhou1, Ji-Na Zheng1, Wen-Yue Liu2, Sven Van Poucke3, Tian-Tian Zou4, Dong-Chu Zhang5, Shengrong Shen6, Ke-Qing Shi7, Xiao-Dong Wang7, Ming-Hua Zheng8. 1. 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. 2. Department of Endocrinology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 3. Department of Anesthesiology, Critical Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium. 4. Department of Hepatology, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of the Second Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China. 5. Wenzhou Medical Center, Wenzhou People's Hospital, Wenzhou, China. 6. Department of Food Science & Nutrition, Zhejiang University, Hangzhou, China. 7. Department of Hepatology, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Institute of Hepatology, Wenzhou Medical University, Wenzhou, China. 8. Department of Hepatology, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Institute of Hepatology, Wenzhou Medical University, Wenzhou, China. Electronic address: zhengmh@wmu.edu.cn.
Abstract
BACKGROUND: Several non-invasive diagnostic scores for non-alcoholic fatty liver (NAFL) have been developed, but the clinical application is limited because of their complexity. AIM: To develop and validate an easy-to-calculate scoring system to identify ultrasound-diagnosed NAFL. METHODS: 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. RESULTS: 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. CONCLUSION: We developed and validated NSS, an easy-to-use score sheet identify ultrasound-diagnosed NAFL. NSS may be clinically useful for initial diagnosing NAFL.
BACKGROUND: Several non-invasive diagnostic scores for non-alcoholic fatty liver (NAFL) have been developed, but the clinical application is limited because of their complexity. AIM: To develop and validate an easy-to-calculate scoring system to identify ultrasound-diagnosed NAFL. METHODS: 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. RESULTS: 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. CONCLUSION: We developed and validated NSS, an easy-to-use score sheet identify ultrasound-diagnosed NAFL. NSS may be clinically useful for initial diagnosing NAFL.