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Ann Thorac Surg 1997;63:1531-1532
© 1997 The Society of Thoracic Surgeons

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Editorial

Cardiothoracic Databases: Where Are We Headed?

Division of Cardiothoracic Surgery, University of Colorado Health Sciences Center, Denver, Colorado

The first 20% of the full text of this article appears below.

Estimating risk for operative death, morbidity, and other outcomes is becoming increasingly important to cardiothoracic surgeons and others in the medical field. This allows us to come closer to measuring quality of care by taking into account patient risk factors. The article by Drs Lippmann and Shahian [1] is an excellent review of several statistical methods for predicting risk-adjusted outcomes. Although it is somewhat complex because of the statistical language, it is very important because it furthers our knowledge of statistical methodology in this evolving area. Of particular interest is the fact that the more complex neural network analysis does not at the present time offer any greater predictive accuracy than does the Bayesian analysis or logistic regression analysis.

Lippmann and Shahian examined 80,606 patients from The Society of Thoracic Surgeons (STS) Database who underwent operation during 1993 and compared logistic regression and the Bayesian analysis with single-, two-, and three-layer neural networks. These were evaluated by equal members of training and cross-validation testing groups generating receiver operating characteristic curves for predicting mortality. The receiver operating characteristic curve areas, or C-indices, were approximately 76% for all of the techniques, but there was some improvement in accuracy as measured by the C-index when this was combined with the best neural network and the logistic regression model, although this difference was not a large one. Of interest is the fact that when the patients were stratified into six risk levels (0% to 2.5%, 2.5% to 5%, 5% to 10%, 10% to 20%, 20% to 30%, and 30% . . . [Full Text of this Article]

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