Risk adjustment using administrative data: impact of a diagnosis-type indicator

J Gen Intern Med. 2001 Aug;16(8):519-24. doi: 10.1046/j.1525-1497.2001.016008519.x.

Abstract

Objectives: To determine the frequency with which commonly coded clinical variables are complications, as opposed to baseline comorbidities, and to compare the results of 2 risk-adjusted outcome analyses for coronary artery bypass graft surgery for which we either (a) ignored, or (b) used the available "diagnosis-type indicator."

Design: Analysis of existing administrative data.

Setting: Twenty-three Canadian hospitals.

Patients: A total of 50,357 coronary artery bypass graft surgery cases.

Measurements and main results: Among 21 clinical variables whose definitions involve the diagnosis-type indicator, 14 were predominantly (> or =97%) baseline risk factors when present. Seven variables were often complication diagnoses: renal disease (when present, 13% coded as complications), recent myocardial infarction (15%), peptic ulcer disease (15%), congestive heart failure (17%), cerebrovascular disease (26%), hemiplegia (34%), and severe liver disease (35%). The results of risk adjustment analyses predicting in-hospital mortality differed when the diagnosis-type indicator was either used or ignored, and as a result, adjusted hospital mortality rates and rankings changed, often dramatically, with rankings increasing for 10 hospitals, decreasing for 9 hospitals, and remaining the same for only 4 hospitals.

Conclusions: The results of analyses performed using the diagnosis-type indicator in Canadian administrative data differ considerably from analyses that ignore the indicator. The widespread introduction of such an indicator should be considered in other countries, because risk-adjustment analyses performed without a diagnosis-type indicator may yield misleading results.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Canada
  • Comorbidity
  • Coronary Artery Bypass / adverse effects*
  • Female
  • Hospital Mortality*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Odds Ratio
  • Outcome Assessment, Health Care / methods*
  • Risk Adjustment*