Chao Cen1, Wenpu Wang2, Songfeng Yu1, Xiaofeng Tang1, Jimin Liu3, Yuanxing Liu1, Lin Zhou1, Jun Yu1, Shusen Zheng4. 1. Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China. 2. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310016, China. 3. Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, 1200 Main Street West, Hamilton, L8S 4K1, Canada. 4. Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China. zyzss@zju.edu.cn.
Abstract
BACKGROUND AND AIM: Nonalcoholic fatty liver disease (NAFLD) is becoming the leading cause of chronic liver disease in China. The early identification and management of patients at risk are essential. We aimed to develop a novel clinical and laboratory-based nomogram (CLN) model to predict NAFLD with high accuracy. METHODS: We designed a retrospective cross-sectional study and enrolled 21,468 participants (16,468 testing and 5000 validation). Clinical information and laboratory/imaging results were retrieved. Significant variables independently associated with NAFLD were identified by a logistic regression model, and a NAFLD prediction CLN was constructed. The CLN was then compared with four existing NAFLD-related prediction models: the fatty liver index (FLI), the hepatic steatosis index (HSI), the visceral adiposity index (VAI) and the triglycerides and glucose (TyG) index. Area under the receiver operator characteristic curve (AUROC) and decision curve analysis (DCA) were performed. RESULTS: A total of 6261/16,468 (38.02%) and 1759/5000 (35.18%) participants in the testing and validation datasets, respectively, had ultrasound-proven NAFLD. Six variables were selected to build the CLN: body mass index (BMI), diastolic blood pressure (DBP), uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), and alanine aminotransferase (ALT). The diagnostic accuracy of the CLN for NAFLD (AUROC 0.857, 95% CI 0.852-0.863) was significantly superior to that of the FLI (AUROC 0.849, 95% CI 0.843-0.855), the VAI (AUROC 0.752, 95% CI 0.745-0.760), the HSI (AUROC 0.828, 95% CI 0.822-0.834), and the TyG index (AUROC 0.774, 95% CI 0.767-0.781) (all p < 0.001). DCA confirmed the clinical utility of the CLN. CONCLUSIONS: This physical examination and laboratory test-based, nonimage-assisted novel nomogram has better performance in predicting NAFLD than the FLI, the VAI, the HSI and the TyG index alone. This model can be used as a quick screening tool to assess NAFLD in the general population.
BACKGROUND AND AIM: Nonalcoholic fatty liver disease (NAFLD) is becoming the leading cause of chronic liver disease in China. The early identification and management of patients at risk are essential. We aimed to develop a novel clinical and laboratory-based nomogram (CLN) model to predict NAFLD with high accuracy. METHODS: We designed a retrospective cross-sectional study and enrolled 21,468 participants (16,468 testing and 5000 validation). Clinical information and laboratory/imaging results were retrieved. Significant variables independently associated with NAFLD were identified by a logistic regression model, and a NAFLD prediction CLN was constructed. The CLN was then compared with four existing NAFLD-related prediction models: the fatty liver index (FLI), the hepatic steatosis index (HSI), the visceral adiposity index (VAI) and the triglycerides and glucose (TyG) index. Area under the receiver operator characteristic curve (AUROC) and decision curve analysis (DCA) were performed. RESULTS: A total of 6261/16,468 (38.02%) and 1759/5000 (35.18%) participants in the testing and validation datasets, respectively, had ultrasound-proven NAFLD. Six variables were selected to build the CLN: body mass index (BMI), diastolic blood pressure (DBP), uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), and alanine aminotransferase (ALT). The diagnostic accuracy of the CLN for NAFLD (AUROC 0.857, 95% CI 0.852-0.863) was significantly superior to that of the FLI (AUROC 0.849, 95% CI 0.843-0.855), the VAI (AUROC 0.752, 95% CI 0.745-0.760), the HSI (AUROC 0.828, 95% CI 0.822-0.834), and the TyG index (AUROC 0.774, 95% CI 0.767-0.781) (all p < 0.001). DCA confirmed the clinical utility of the CLN. CONCLUSIONS: This physical examination and laboratory test-based, nonimage-assisted novel nomogram has better performance in predicting NAFLD than the FLI, the VAI, the HSI and the TyG index alone. This model can be used as a quick screening tool to assess NAFLD in the general population.
Entities:
Keywords:
Early diagnosis and prevention; Fatty liver index; Healthy population; Hepatic steatosis index; Liver biopsy; Metabolic syndrome; Nonimage assisted; Prediction model; Triglycerides and glucose index; Visceral adiposity index