Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Northern New England Cardiovascular Disease Study Group.

Abstract

BACKGROUND: A prospective regional study was conducted to identify factors associated with in-hospital mortality among patients undergoing isolated coronary artery bypass graft surgery (CABG). A prediction rule was developed and validated based on the data collected. METHODS AND RESULTS: Data from 3,055 patients were collected from five clinical centers between July 1, 1987, and April 15, 1989. Logistic regression analysis was used to predict the risk of in-hospital mortality. A prediction rule was developed on a training set of data and validated on an independent test set. The metric used to assess the performance of the prediction rule was the area under the relative operating characteristic (ROC) curve. Variables used to construct the regression model of in-hospital mortality included age, sex, body surface area, presence of comorbid disease, history of CABG, left ventricular end-diastolic pressure, ejection fraction score, and priority of surgery. The model significantly predicted the occurrence of in-hospital mortality. The area under the ROC curve obtained from the training set of data was 0.74 (perfect, 1.0). The prediction rule performed well when used on a test set of data (area, 0.76). The correlation between observed and expected numbers of deaths was 0.99. CONCLUSIONS: The prediction rule described in this report was developed using regional data, uses only eight variables, has good performance characteristics, and is easily available to clinicians with access to a microcomputer or programmable calculator. This validated multivariate prediction rule would be useful both to calculate the risk of mortality for an individual patient and to contrast observed and expected mortality rates for an institution or a particular clinician.