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Ann Thorac Surg 1994;57:1492-1500
© 1994 The Society of Thoracic Surgeons

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Articles

Bayesian-logit model for risk assessment in coronary artery bypass grafting

Denver Department of Veterans Affairs Medical Center, Department of Preventive Medicine and Biometrics, Department of Medicine, and Division of Cardiothoracic Surgery, University of Colorado Health Sciences Center, Denver, Colorado, USA

^_* Address reprint requests to Dr Marshall, Cardiac Research (151), Department of Veterans Affairs Medical Center, 1055 Clermont, Denver, CO 80220.

	Abstract

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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 Bayesianlogit 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.

	Footnotes

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Presented at the Fortieth Annual Meeting of the Southern Thoracic Surgical Association, Panama City Beach, FL, Nov 4–6, 1993.

This work was supported by the Health Services Research and Development Service and the Office of Quality Management of the Department of Veterans Affairs.

	References

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1. Hammermeister KE, Johnson R, Marshall G, Grover FL. Continuous assessment and improvement in quality of care: a model from the Department of Veterans Affairs cardiac surgery Ann Surg 1994;219:281-289.[Medline]
2. Hannan EL, Kilburn H, O'Donnell JF, Lukacik G, Shields EP. Adult open heart surgery in New York State: an analysis of risk factors and hospital mortality rates JAMA 1990;264:2768-2774.[Medline]
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7. Marshall G, Grover FL, Henderson WG, Hammermeister KE. Assessment of predictive models for binary outcomes: an empirical approach using operative death from cardiac surgery Statistics Med 1994(in press).
8. Cook EF, Goldman L. Empiric comparison of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis J Chronic Dis 1984;37:721-731.[Medline]
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12. Grover FL, Hammermeister KE, Burchfiel C, Cardiac Surgeons of the Department of Veterans Affairs. Initial report of the Veterans Administration preoperative risk assessment study for cardiac surgery Ann Thorac Surg 1990;50:12-28.[Abstract/Free Full Text]
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