A Quality Assurance Model of Operative Mortality in Coronary Artery Surgery

Abstract

Quality assurance in coronary artery bypass grafting (CABG) surgery requires a comparison of operative mortality against an accepted standard of care. Raw mortality statistics are unacceptable in this context, and risk factor analysis is essential. However, this principle has not been adequately demonstrated in previous reports. Our goal in this study was to develop a risk model of accepted CABG mortality and illustrate its proper use in coronary artery surgery. The model was derived from a Bayesian analysis of 6.630 patients undergoing CABG in the Coronary Artery Surgery Study (CASS) registry. Age, sex, ventricular function, previous myocardial infraction, extent of coronary artery disease, unstable angina, and surgical priority were used by the model to sort patients into risk categories. These results illustrate the fallacy of using raw mortality statistics for interinstitutional comparisons. This type of risk model is a fundamental element of CABG quality assurance. Keywords: Reprints; Risk assessment. (KT)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA212635

Entities

People

  • Fred H. Edwards
  • Geoffrey M. Graeber
  • Michael A Barry
  • Robert A. Albus
  • Rostik Zajtchuk

Organizations

  • Walter Reed Army Institute of Research

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programs
  • Confidence Limits
  • Data Science
  • Databases
  • Health Services
  • Information Science
  • Medical Personnel
  • Myocardial Ischemia
  • Numbers
  • Physicians
  • Probability
  • Risk
  • Risk Factors
  • Statistical Analysis
  • Statistical Data
  • Surgery

Fields of Study

  • Medicine

Readers

  • Cardiovascular Physiology
  • Theoretical Analysis.
  • Trauma or Military Medicine

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference