Clinical experience and predicting survival in coronary disease.

To study the accuracy with which long-term prognosis can be predicted in patients with coronary artery disease, prognostic predictions obtained from a large, diverse sample of practicing cardiologists were compared with predictions from a multivariable statistical model. Test samples of 10 patients each were selected from a large series of medically treated patients with significant coronary disease. Using detailed clinical summaries, 49 cardiologists each predicted the probability of 3-year survival and infarction-free survival for 10 patients. Cox regression models, developed using patients who were not in the test samples, were also used to predict corresponding outcome probabilities for each test patient. Overall, the model estimates of prognosis were significantly better than the doctors' predictions. The rank correlation of model predictions with 3-year survival was 0.60, compared with 0.52 for the physicians. Model predictions added significant prognostic information to the doctors' predictions, whereas the converse was not true. Where predictions were made by multiple doctors, the inter-physician variability was substantial. Neither practice characteristics nor extent of clinical experience significantly affected the physicians' predictive accuracy. In coronary artery disease, statistical models developed from carefully collected data can provide prognostic predictions that are more accurate than predictions of experienced clinicians made from detailed case summaries.

Duke Scholars

Author David Franklin Kong Medicine, Cardiology

Author Kerry L. Lee Biostatistics & Bioinformatics, Division of Biostatistics

Author Daniel Benjamin Mark Medicine, Cardiology