BACKGROUND AND AIMS:
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification
methods. The present study sought to design a classification algorithm for NAFLD within the electronic medical record (EMR) for the development of large-scale longitudinal cohorts.

METHODS:
We implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart
reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P < 0.05 were included in
the multivariable analysis.

RESULTS:
The NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD, and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with
AUC of 0.85 versus 0.75 (P < 0.0001) and leads to the creation of a new independent cohort of 8458 individuals with a high probability for NAFLD.

CONCLUSIONS:
The NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy, and can be applied across different institutions to create EMR-based cohorts of individuals with NAFLD.

http://www.ncbi.nlm.nih.gov/pubmed/26537487

http://well.blogs.nytimes.com/2014/06/13/threat-grows-from-liver-illness-tied-to-obesity/

http://www.eurekalert.org/pub_releases/2015-12/hud-sot120815.php

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