NFI, a clinical scoring tool for predicting non-alcoholic fatty liver in the Chinese population

Abstract

Objectives: Accurate assessment of early non-alcoholic fatty liver disease (NAFLD) is important to reduce the possible complications. The purpose of the present study was to develop a simple algorithm for the screening of NAFLD in the Chinese population based on routine anthropometric data and laboratory tests.

Study design: This is a cross-sectional design.

Methods: The subjects (1145) underwent routine physical examinations. The variables in the NAFLD index (NFI) were obtained by a stepwise multiple logistic regression analysis on 1000 bootstrap samples. The area under the receiver-operating characteristic (AUROC) was used to evaluate the accuracy of the NFI.

Results: Multivariate analysis showed that body mass index, fasting blood glucose, ratio of alanine aminotransferase to aspartate aminotransferase, and triglyceride were included in the final equation. The AUROC of the NFI was 0.919 (95% confidence interval = 0.901-0.937). An NFI of <31.0 excluded the possibility of NAFLD with a sensitivity of 96.9%, and at a value of >36.0, the NFI could detect NAFLD with a specificity of 98.9%.

Conclusions: NFI was a cost-effective NAFLD-screening model, which had a high accuracy for predicting NAFLD at early stages in the Chinese population.

Keywords: Non-alcoholic fatty liver disease; Predictive model; Validation.