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Nonalcoholic fatty liver disease (NAFLD) in the Veterans Administration population: development and validation of an algorithm for NAFLD using automated data
Author(s) -
Husain N.,
Blais P.,
Kramer J.,
Kowalkowski M.,
Richardson P.,
ElSerag H. B.,
Kanwal F.
Publication year - 2014
Publication title -
alimentary pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.308
H-Index - 177
eISSN - 1365-2036
pISSN - 0269-2813
DOI - 10.1111/apt.12923
Subject(s) - medicine , algorithm , nonalcoholic fatty liver disease , fatty liver , gastroenterology , liver biopsy , population , liver disease , disease , biopsy , environmental health , computer science
Summary 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.