Risk assessment in urgent/emergent coronary artery surgery

A statistical model has been developed to allow for prediction of individual patient prognosis following urgent/emergent coronary artery bypass grafting (CABG). None of the models previously described for use in coronary artery surgery has been tested with a prospective patient series that confirms the true predictive capacity of the model. Ideally, the predictive ability of such models should be validated with prospective trials. To examine the feasibility of statistical modeling in this clinical context, a computerized model based on the theorem of Bayes was developed to predict operative mortality for urgent coronary artery surgery. The presence or absence of 20 risk factors was determined for each of 405 consecutive patients undergoing urgent coronary artery surgery from January 1984 to January 1989. The first 100 patients were used to develop a database for the model, which was then used to prospectively evaluate the remaining 305 patients. There was good agreement between predicted and observed results. Models of this kind are particularly advantageous because of the ability to (1) accommodate multiple risk factors, (2) become tailored to a specific practice, and (3) determine individual rather than group prognosis. Validation with a prospective trial confirms the practical utility of this approach. This model has reliably predicted the risk associated with urgent coronary artery surgery and may provide important clinical information for the management of patients being evaluated for urgent revascularization.