title: Should we add clinical variables to administrative data?: The case of risk-adjusted case fatality rates after admission for acute myocardial infarction.
abstract: BACKGROUND
  Previous studies have evaluated whether the addition of multiple laboratory and clinical factors to administrative data, or reabstraction of administrative data, improve the accuracy of risk adjustment. This study assessed if a more feasible strategy of adding 3 readily accessible clinical variables to hospital administrative data might improve the risk adjustment for interhospital comparisons.
  
  
  OBJECTIVE
  We compared 3 alternative risk adjustment models for 30-day case-fatality rates (CFR) after admission for acute myocardial infarction (AMI): (1) administrative model (age, sex, and comorbidities); (2) clinical-augmented administrative model (administrative data plus 3 clinical variables: systolic blood pressure, heart rate, and ECG characteristics on admission); and (3) clinical-demographic model (3 clinical variables plus age and sex).
  
  
  METHODS
  Retrospective analysis of matched administrative and clinical datasets.
  
  
  METHODS
  A total of 1743 patients admitted to 21 hospitals in Queensland, Australia, with a principal diagnosis of AMI between January 1, 2003 and December 31, 2005.
  
  
  RESULTS
  There was only fair agreement between the administrative model and the clinical-augmented administrative model (weighted kappa = 0.66). Only 68.7% of the risk-adjusted CFR were in the same decile of risk; 9.9% were 3 or more deciles apart. The clinical-augmented model reduced extrabinomial variation and slightly improved discrimination (c = 0.83 vs. 0.79, P = 0.01). In contrast, removing comorbidities from the clinical model did not alter performance greatly: similar discrimination (c = 0.80 vs. 0.83, P = 0.07), excellent agreement for predicted CFR (weighted kappa = 0.82), and no extrabinomial variation for either model.
  
  
  CONCLUSIONS
  Addition of only 3 readily accessible clinical variables to administrative data improves the risk adjustment for interhospital comparisons of AMI case-fatality rates.
authors.affiliation: Epidemiology Services Unit, Queensland Health, The University of Queensland, Brisbane, Australia.
authors.forename: Trisha C
authors.initials: TC
authors.lastname: Johnston
authors.forename: Michael D
authors.initials: MD
authors.lastname: Coory
authors.forename: Ian
authors.initials: I
authors.lastname: Scott
authors.forename: Stephen
authors.initials: S
authors.lastname: Duckett
mesh_terms: D000367:Age Factors; D000368:Aged; D001794:Blood Pressure; D015897:Comorbidity; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000401:mortality*; D010343:Patient Admission / Q000706:statistics & numerical data*; D020379:Risk Adjustment; D012737:Sex Factors