Risk stratification of in-hospital mortality for coronary artery bypass graft surgery

J Am Coll Cardiol. 2006 Feb 7;47(3):661-8. doi: 10.1016/j.jacc.2005.10.057. Epub 2006 Jan 4.

Abstract

Objectives: The purpose of this research was to develop a risk index for in-hospital mortality for coronary artery bypass graft (CABG) surgery.

Background: Risk indexes for CABG surgery are used to assess patients' operative risk as well as to profile hospitals and surgeons. None has been developed using data from a population-based region in the U.S. for many years.

Methods: Data from New York's Cardiac Surgery Reporting System in 2002 were used to develop a statistical model that predicts mortality and to create a risk index based on a relatively small number of patient risk factors. The fit of the index was tested by applying it to another year (2003) of New York data and testing the correspondence of expected and observed mortality rates for each risk score in the index.

Results: The risk index contains a total of 10 risk factors (age, female gender, hemodynamic state, ejection fraction, pre-procedural myocardial infarction, chronic obstructive pulmonary disease, calcified ascending aorta, peripheral arterial disease, renal failure, and previous open heart operations). The score possible for each variable ranges from 0 to 5, and total risk scores possible range from 0 to 34. The highest score observed for any patient was 22, and 93% of the patients had scores of 8 or lower. When the risk index was applied to another year of New York data with a considerably lower mortality rate, the C-statistic was 0.782.

Conclusions: The risk index appears to be a valuable tool for predicting patient risk when applied to another year of New York data. It should now be tested against other risk indexes in a variety of geographical regions.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Coronary Artery Bypass / mortality*
  • Coronary Disease / complications
  • Coronary Disease / physiopathology
  • Female
  • Hospital Mortality*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Models, Statistical*
  • New York / epidemiology
  • Risk Assessment
  • Risk Factors