title: Use of administrative data to identify colorectal liver metastasis.
abstract: BACKGROUND
  The ability to identify patients with colorectal cancer (CRC) liver metastasis (LM) using administrative data is unknown. The goals of this study were to evaluate whether administrative data can accurately identify patients with CRCLM and to develop a diagnostic algorithm capable of identifying such patients.
  
  
  METHODS
  A retrospective cohort study was conducted to validate the diagnostic and procedural codes found in administrative databases of the Veterans Administration (VA) system. CRC patients evaluated at a major VA center were identified (1997-2008, n = 1671) and classified as having liver-specific ICD-9 and/or CPT codes. The presence of CRCLM was verified by primary chart abstraction in the study sample. Contingency tables were created and the positive predictive value (PPV) for CRCLM was calculated for each candidate administrative code. A multivariate logistic-regression model was used to identify independent predictors (codes) of CRCLM, which were used to develop a diagnostic algorithm. Validity of the algorithm was determined by discrimination (c-statistic) of the model and PPV of the algorithm.
  
  
  RESULTS
  Multivariate logistic regression identified ICD-9 diagnosis codes 155.2 (OR 9.7 [95% CI 2.5-38.4]) and 197.7 (84.6 [52.9-135.3]), and procedure code 50.22 (5.9 [1.3-25.5]) as independent predictors of CRCLM diagnosis. The model's discrimination was 0.89. The diagnostic algorithm, defined as the presence of any of these codes, had a PPV of 87%.
  
  
  CONCLUSIONS
  VA administrative databases reliably identify patients with CRCLM. This diagnostic algorithm is highly predictive of CRCLM diagnosis and can be used for research studies evaluating population-level features of this disease within the VA system.
authors.affiliation: VA HSR & D Houston Center of Excellence, Michael E. DeBakey Veterans Affairs Medical Center, Baylor College of Medicine, Houston, Texas 77030, USA. danaya@bcm.edu
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Anaya
authors.forename: Natasha S
authors.initials: NS
authors.lastname: Becker
authors.forename: Peter
authors.initials: P
authors.lastname: Richardson
authors.forename: Neena S
authors.initials: NS
authors.lastname: Abraham
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D015179:Colorectal Neoplasms / Q000473:pathology*; D039702:Current Procedural Terminology; D019992:Databases as Topic / Q000145:classification*; D005260:Female; D006298:Health Services Administration / Q000145:classification*; D006801:Humans; D038801:International Classification of Diseases; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000556:secondary*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014481:United States; D014493:United States Department of Veterans Affairs