Abstract
OBJECTIVE: The aim of this study was to establish a simple, nonalcoholic fatty liver disease (NAFLD) screening model using readily available variables to identify high-risk individuals in Western Xinjiang, China. METHODS: A total of 40 033 patients from the National Health Examination were divided into a training group (70%) and a validation group (30%). Univariate regression and least absolute shrinkage and selection operator models optimized feature selection, while a multivariate logistic regression analysis constructed the prediction model. The model's performance was evaluated using the area under the receiver operating characteristic curve, and its clinical utility was assessed through decision curve analysis. RESULTS: The nomogram assessed NAFLD risk based on factors such as sex, age, diastolic blood pressure, waist circumference, BMI, fasting plasma glucose, alanine aminotransferase, platelet count, total cholesterol, triglycerides, low-density lipoprotein-cholesterol, and high-density lipoprotein-cholesterol. The area under the receiver operating characteristic curves were 0.829 for men and 0.859 for women in the development group, and 0.817 for men and 0.865 for women in the validation group. The decision curve analysis confirmed the nomogram's clinical usefulness, with consistent findings in the validation set. CONCLUSION: A user-friendly nomogram prediction model for NAFLD risk was successfully developed and validated for Western Xinjiang, China.