title: Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record.
abstract: OBJECTIVE
  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.
authors.affiliation: Gastrointestinal Unit, Massachusetts General Hospital, 55 Fruit Street, Blake 4, Boston, MA, 02114, USA. kcorey@partners.org.|Harvard Medical School, Boston, MA, USA. kcorey@partners.org.
authors.forename: Kathleen E
authors.identifier: http://orcid.org/0000-0003-2882-7264
authors.initials: KE
authors.lastname: Corey
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Uri
authors.initials: U
authors.lastname: Kartoun
authors.affiliation: Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
authors.forename: Stanley Y
authors.initials: SY
authors.lastname: Shaw
mesh_terms: D000328:Adult; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D001219:Aspartate Aminotransferases / Q000097:blood; D001706:Biopsy; D015331:Cohort Studies; D003625:Data Collection; D003920:Diabetes Mellitus / Q000453:epidemiology; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D065626:Non-alcoholic Fatty Liver Disease / Q000097:blood / Q000453:epidemiology; D015995:Prevalence; D014280:Triglycerides / Q000097:blood; D014481:United States / Q000453:epidemiology
chemical_list: D014280:Triglycerides; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase
keywords: Electronic medical records; Nonalcoholic fatty liver disease; Nonalcoholic steatohepatitis; Triglycerides