Predicting outcome in XXXX . Statistical models versus expert clinicians.

@entity1 @entity453 @entity797 @entity301
To study the accuracy with which long-term prognosis can be predicted in @entity1 with @entity453 , prognostic predictions from a data-based multivariable statistical model were compared with predictions from senior clinical cardiologists. Test samples of 100 @entity1 each were selected from a large series of medically treated @entity1 with significant @entity301 . Using detailed case summaries, five senior cardiologists each predicted one- and three-year survival and infarct-free survival probabilities for 100 @entity1 . Fifty @entity1 appeared in multiple samples for assessing interphysician variability. @entity797 regression models, developed using @entity1 not in the test samples, predicted corresponding outcome probabilities for each test @entity1 . Overall, model predictions correlated better with actual @entity1 outcomes than did the doctors' predictions. For three-year survival, rank correlations were 0.61 (model) and 0.49 (doctors). For three-year infarct-free survival predictions, correlations with outcome were 0.48 (model) and 0.29 (doctors). Comparisons by individual doctor revealed @entity797 model three-year survival predictions were better than those of four of five doctors (model predictions added significant [p less than 0.05] prognostic information to the doctor's predictions, whereas the converse was not true). For infarct-free survival, the @entity797 model was superior to all five doctors. Where predictions were made by multiple doctors, the interphysician variability was substantial. In @entity453 , 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.
