A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem

Abstract

Objective: To evaluate the performance of a diagnostic model based on a composite index using clinical and laboratory data, including cardiovascular biomarkers, to help practitioners to differentiate patients with simple steatosis from those with nonalcoholic steatohepatitis (NASH). Design and methods: 101 patients with biopsy proven features of nonalcoholic fatty liver disease were included. We investigated the usefulness of 9 biomarkers in predicting the histological disease severity, including routine biochemical tests, C-reactive protein, soluble intercellular adhesion molecule-1 (sICAM-1) and anthropometric evaluation. Receiver operating characteristic (ROC) curves and likelihood ratios (LRs) were used to evaluate the fit of each test. A composite index was calculated as the product of each individual test LR. Results: In a model patient who has all positive tests, the post-test probability for NASH would be 99.5%. Conclusion: The capacity of each individual biomarker to independently predict the disease outcome was lower than a composite index constructed after multiplying the LR for each individual test combined into a “multimarker” score.