Nonalcoholic fatty liver disease (NAFLD) in the Veterans Administration population: development and validation of an algorithm for NAFLD using automated data

Abstract

BACKGROUND: In practice, nonalcoholic fatty liver disease (NAFLD) is diagnosed based on elevated liver enzymes and confirmatory liver biopsy or abdominal imaging. Neither method is feasible in identifying individuals with NAFLD in a large-scale healthcare system. AIM: To develop and validate an algorithm to identify patients with NAFLD using automated data. METHODS: Using the Veterans Administration Corporate Data Warehouse, we identified patients who had persistent ALT elevation (≥2 values ≥40 IU/mL ≥6 months apart) and did not have evidence of hepatitis B, hepatitis C or excessive alcohol use. We conducted a structured chart review of 450 patients classified as NAFLD and 150 patients who were classified as non-NAFLD by the database algorithm, and subsequently refined the database algorithm. RESULTS: The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) for the initial database definition of NAFLD were 78.4% (95% CI: 70.0-86.8%), 74.5% (95% CI: 68.1-80.9%), 64.1% (95% CI: 56.4-71.7%) and 85.6% (95% CI: 79.4-91.8%), respectively. Reclassifying patients as having NAFLD if they had two elevated ALTs that were at least 6 months apart but within 2 years of each other, increased the specificity and PPV of the algorithm to 92.4% (95% CI: 88.8-96.0%) and 80.8% (95% CI: 72.5-89.0%), respectively. However, the sensitivity and NPV decreased to 55.0% (95% CI: 46.1-63.9%) and 78.0% (95% CI: 72.1-83.8%), respectively. CONCLUSIONS: Predictive algorithms using automated data can be used to identify patients with NAFLD, determine prevalence of NAFLD at the system-wide level, and may help select a target population for future clinical studies in veterans with NAFLD.