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Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions
Quantitative Health Sciences Publications and Presentations
  • Lisa I. Iezzoni, Harvard Medical School
  • Arlene S. Ash, University of Massachusetts Medical School
  • Michael Shwartz, Boston University
  • Bruce E. Landon, Harvard Medical School
  • Yevgenia D. Mackiernan, Harvard Medical School
UMMS Affiliation
Department of Quantitative Health Sciences
Document Type
Medical Subject Headings
Coronary Artery Bypass; Female; Health Services Research; *Hospital Mortality; Humans; Logistic Models; Male; Middle Aged; Odds Ratio; Patient Discharge; Predictive Value of Tests; Reproducibility of Results; Risk Assessment; Risk Factors; *Severity of Illness Index; United States
OBJECTIVES: Severity-adjusted death rates for coronary artery bypass graft (CABG) surgery by provider are published throughout the country. Whether five severity measures rated severity differently for identical patients was examined in this study. METHODS: Two severity measures rate patients using clinical data taken from the first two hospital days (MedisGroups, physiology scores); three use diagnoses and other information coded on standard, computerized hospital discharge abstracts (Disease Staging, Patient Management Categories, all patient refined diagnosis related groups). The database contained 7,764 coronary artery bypass graft patients from 38 hospitals with 3.2% in-hospital deaths. Logistic regression was performed to predict deaths from age, age squared, sex, and severity scores, and c statistics from these regressions were used to indicate model discrimination. Odds ratios of death predicted by different severity measures were compared. RESULTS: Code-based measures had better c statistics than clinical measures: all patient refined diagnosis related groups, c = 0.83 (95% C.I. 0.81, 0.86) versus MedisGroups, c = 0.73 (95% C.I. 0.70, 0.76). Code-based measures predicted very different odds of dying than clinical measures for more than 30% of patients. Diagnosis codes indicting postoperative, life-threatening conditions may contribute to the superior predictive power of code-based measures. CONCLUSIONS: Clinical and code-based severity measures predicted different odds of dying for many coronary artery bypass graft patients. Although code-based measures had better statistical performance, this may reflect their reliance on diagnosis codes for life-threatening conditions occurring late in the hospitalization, possibly as complications of care. This compromises their utility for drawing inferences about quality of care based on severity-adjusted coronary artery bypass graft death rates.
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Citation: Med Care. 1998 Jan;36(1):28-39. Link to article on publisher's site
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Link to Article in PubMed
Citation Information
Lisa I. Iezzoni, Arlene S. Ash, Michael Shwartz, Bruce E. Landon, et al.. "Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions" Vol. 36 Iss. 1 (1998) ISSN: 0025-7079 (Linking)
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