External Validation and Clinical Utility of Non-Invasive Prediction Models for Non-Alcoholic Fatty Liver Disease in Malaysia

Abstract Background: Many prediction models have been developed to detect non-alcoholic fatty liver disease (NAFLD). The drawbacks of many of models are the use of parameters that are not routinely measured locally. This study aimed to evaluate the external validity of a series of prediction models for NAFLD, which were selected based on the routinely measured and tested clinical parameters in public healthcare centers in Malaysia. Methods: A literature search of articles that described the prediction models for NAFLD on adult subjects between 2000 and 2019 was conducted. The validation cohort comprised patients who underwent liver elastography using the Fibroscan® device in a public tertiary care center between January 2017 and December 2019. Both the discrimination and calibration of each model were assessed to determine their predictive performance. Results: Out of the 404 patients undergoing liver elastography, 280 were diagnosed with NAFLD (69.3%). Six prediction models were identified from the existing literature and evaluated. The calibration assessment demonstrated that although three of the models overestimated the NAFLD risk, updating the models generally improved their calibration performance. The discriminative performance of the selected models ranged from 0.717 to 0.783. With a specificity level of 90% and 80%, the sensitivity of all the models fell between 31.1%–48.9% and 46.4%–66.8%, respectively. The Framingham Steatosis Index (FSI) model demonstrated a better predictive performance compared to the other models. Conclusions: The FSI model demonstrates an acceptable predictive performance. Its application in clinical practice could promote the screening and early treatment of NAFLD in the Malaysian population.