title: An administrative data merging solution for dealing with missing data in a clinical registry: adaptation from ICD-9 to ICD-10.
abstract: BACKGROUND
  We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance.
  
  
  METHODS
  A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995-2001 based on the ICD-9-CM coding algorithm, and for 2002-2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995-2005. The corresponding administrative data records were coded in ICD-9-CM for 1995-2001 and in ICD-10 for 2002-2005. The resulting datasets were then evaluated for their ability to predict death at one year.
  
  
  RESULTS
  The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit.
  
  
  CONCLUSIONS
  The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada. dasouthe@ucalgary.ca
authors.forename: Danielle A
authors.initials: DA
authors.lastname: Southern
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Norris
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
authors.forename: Fiona M
authors.initials: FM
authors.lastname: Shrive
authors.forename: P Diane
authors.initials: PD
authors.lastname: Galbraith
authors.forename: Karin
authors.initials: K
authors.lastname: Humphries
authors.forename: Min
authors.initials: M
authors.lastname: Gao
authors.forename: Merril L
authors.initials: ML
authors.lastname: Knudtson
authors.forename: William A
authors.initials: WA
authors.lastname: Ghali
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D006328:Cardiac Catheterization / Q000401:mortality / Q000706:statistics & numerical data; D015897:Comorbidity; D003625:Data Collection; D006801:Humans; D038801:International Classification of Diseases; D008499:Medical Records / Q000145:classification; D008875:Middle Aged; D015233:Models, Statistical; D017202:Myocardial Ischemia / Q000145:classification* / Q000401:mortality / Q000628:therapy; D012042:Registries / Q000592:standards / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors