Development and validation of a risk score for detecting non-alcoholic fatty liver disease.

Abstract

The development of an easy-to-use noninvasive model to screen nonalcoholic fatty liver disease (NAFLD) is warranted. This study aimed to develop and validate a simple noninvasive NAFLD risk score (NARS). We used the National Health and Nutrition Examination Survey 2017 to March 2020 cycle data. The sample size of derivation and validation cohort were 4056 and 2502, separately. The NAFLD was determined by FibroScan® measured controlled attenuation parameter scores of >285 dB/m in the absence of excessive alcohol use, steatogenic medications use, and viral hepatitis. The NARS was derived from a multivariable logistic regression model and variables were selected based on Boruta analysis. The performance of NARS was internally validated and compared with previous models using receiver-operating characteristics curve and C-statistics. The NARS was established using waist circumference, triglycerides, alanine aminotransferase, and fasting glucose, and the total score ranges from 0 to 8, with an increasing risk of NAFLD. NARS demonstrated ideal discrimination in the validation cohort, with C-statistics of 0.832 (95% confidence interval, 0.801-0.824), and was not inferior to any existing models. The optimal cutoff point for predicting NAFLD was obtained at 4 scores with a sensitivity of 82% and specificity of 69%. We reported the derivation and internal validation of a novel and easy-to-use risk score for detecting the presence of NAFLD. NARS demonstrated ideal discrimination performance and was practical in clinical practice for selecting individuals at higher risk of NAFLD for further examination or intervention.