A comparison of four severity-adjusted models to predict mortality after coronary artery bypass graft surgery

R. K. Orr, B. S. Maini, F. D. Sottile, E. M. Dumas and P. O'Mara
Department of Surgery, Fallon Healthcare System, Worcester, Mass.

OBJECTIVE: To assess the validity of four severity-adjusted models to predict mortality following coronary artery bypass graft surgery by using an independent surgical database. DESIGN: A prospective observational study wherein predicted mortality for each patient was obtained by using four different published severity-adjusted models. SETTING: A university-affiliated teaching community hospital. PATIENTS: Eight hundred sixty-eight consecutive patients who underwent coronary artery bypass graft surgery without accompanying valve or aneurysm repair during the period from 1991 to 1993. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Predicted mortality rates for each model were obtained by averaging individual patient predictions and were compared with actual morality rates. We assessed the accuracy of overall prediction for the total series, as well as compared individual patient predictions created by each model. The discrimination of models was assessed with receiver operating characteristic curves and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The observed crude mortality rate was 3.7%. The predicted mortality rate ranged from 2.8% to 9.2%, despite relatively good discrimination by the models (area under the receiver operating characteristic curve, 0.70 to 0.74). The individual patient mortality predicted by different models varied by as much as a ninefold difference. CONCLUSIONS: The currently used coronary artery bypass graft predictive models, although generally accurate, have significant shortcomings and should be used with caution. The predicted mortality rate following coronary artery bypass graft surgery varied by a factor of 3.3 from lowest to highest, making the choice of model a critical factor when assessing outcome. The use of these models for individual patient risk estimations is risky because of the marked discrepancies in individual predictions created by each model.

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