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The Annals of Thoracic Surgery, Vol 57, 1492-1499, Copyright © 1994 by The Society of Thoracic Surgeons
G Marshall, AL Shroyer, FL Grover and KE Hammermeister
Predictive models for the assessment of operative risk using patient risk
factors have gained popularity in the medical community as an important
tool for the adjustment of surgical outcome. The Bayes' theorem model is
among the various models used to predict mortality among patients
undergoing coronary artery bypass grafting procedures. Comparative studies
of the various classic statistical techniques, such as logistic regression,
cluster of variables followed by a logistic regression, a subjectively
created sickness score, classification trees model, and the Bayes' theorem
model, have shown that the Bayes' model is among those with the highest
predictive power. In this study, the Bayes' theorem model is reformulated
as a logistic equation and extended to include qualitative and quantitative
risk factors. We show that the resulting model, the Bayesian-logit model,
is a mixture of logistic regression and linear discriminant analysis. This
new model can be created easily without complex computer programs. Using
12,712 patients undergoing coronary artery bypass grafting procedures at
the Department of Veterans Affairs Continuous Improvement in Cardiac
Surgery Study between April 1987 and March 1990, the predictive power of
the Bayesian-logit model is compared with the Bayes' theorem model,
logistic regression, and discriminant analysis. The ability of the
Bayesian-logit model to discriminate between operative deaths and operative
survivors is comparable with that of logistic regression and discriminant
analysis.
ARTICLES
Bayesian-logit model for risk assessment in coronary artery bypass grafting
Denver Department of Veterans Affairs Medical Center, University of Colorado Health Sciences Center, Denver.
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