title: Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?
abstract: OBJECTIVE
  To determine if a widely used proprietary risk-adjustment system, APR-DRGs, misadjusts for severity of illness and misclassifies provider performance.
  
  
  METHODS
  (1) Discharge abstracts for 116,174 noninstitutionalized adults with acute myocardial infarction (AMI) admitted to nonfederal California hospitals in 1991-1993; (2) inpatient medical records for a stratified probability sample of 974 patients with AMIs admitted to 30 California hospitals between July 31, 1990 and May 31, 1991.
  
  
  METHODS
  Using the 1991-1993 data set, we evaluated the predictive performance of APR-DRGs Version 12. Using the 1990/1991 validation sample, we assessed the effect of assigning APR-DRGs based on different sources of ICD-9-CM data.
  
  
  METHODS
  Trained, blinded coders reabstracted all ICD-9-CM diagnoses and procedures, and established the timing of each diagnosis. APR-DRG Risk of Mortality and Severity of Illness classes were assigned based on (1) all hospital-reported diagnoses, (2) all reabstracted diagnoses, and (3) reabstracted diagnoses present at admission. The outcome variables were 30-day mortality in the 1991-1993 data set and 30-day inpatient mortality in the 1990/1991 validation sample.
  
  
  RESULTS
  The APR-DRG Risk of Mortality class was a strong predictor of death (c = .831-.847), but was further enhanced by adding age and sex. Reabstracting diagnoses improved the apparent performance of APR-DRGs (c = .93 versus c = .87), while using only the diagnoses present at admission decreased apparent performance (c = .74). Reabstracting diagnoses had less effect on hospitals' expected mortality rates (r = .83-.85) than using diagnoses present at admission instead of all reabstracted diagnoses (r = .72-.77). There was fair agreement in classifying hospital performance based on these three sets of diagnostic data (K = 0.35-0.38).
  
  
  CONCLUSIONS
  The APR-DRG Risk of Mortality system is a powerful risk-adjustment tool, largely because it includes all relevant diagnoses, regardless of timing. Although some late diagnoses may not be preventable, APR-DRGs appear suitable only if one assumes that none is preventable.
authors.affiliation: University of California, Davis, School of Medicine Division of General Medicine, Sacramento 95817, USA.
authors.forename: P S
authors.initials: PS
authors.lastname: Romano
authors.forename: B K
authors.initials: BK
authors.lastname: Chan
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D015982:Bias; D002140:California / Q000453:epidemiology; D015897:Comorbidity; D003953:Diagnosis-Related Groups / Q000145:classification* / Q000592:standards*; D006302:Health Services Research; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D009203:Myocardial Infarction / Q000150:complications / Q000401:mortality*; D017063:Outcome Assessment, Health Care / Q000458:organization & administration; D010351:Patient Discharge / Q000706:statistics & numerical data / Q000639:trends; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D020379:Risk Adjustment / Q000379:methods*; D012307:Risk Factors; D012720:Severity of Illness Index; D017678:Sex Distribution; D016037:Single-Blind Method; D012986:Software Validation