title: ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction.
abstract: BACKGROUND
  With the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias.
  
  
  METHODS
  Coders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality.
  
  
  RESULTS
  Compared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1-100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994-2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81).
  
  
  CONCLUSIONS
  The ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases.
authors.affiliation: Department of Community Health Sciences and Centre for Health and Policy Studies, University of Calgary, Calgary, Alberta, Canada. lso@ucalgary.ca <lso@ucalgary.ca>
authors.forename: Lawrence
authors.initials: L
authors.lastname: So
authors.forename: Dewey
authors.initials: D
authors.lastname: Evans
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
mesh_terms: D000328:Adult; D000368:Aged; D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D015897:Comorbidity; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008485:Medical Audit; D008499:Medical Records; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000453:epidemiology* / Q000401:mortality; D015995:Prevalence; D011379:Prognosis; D020379:Risk Adjustment; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors