Predicting outcome in coronary disease. Statistical models versus expert clinicians.

Published

Journal Article

To study the accuracy with which long-term prognosis can be predicted in patients with coronary artery disease, prognostic predictions from a data-based multivariable statistical model were compared with predictions from senior clinical cardiologists. Test samples of 100 patients each were selected from a large series of medically treated patients with significant coronary disease. Using detailed case summaries, five senior cardiologists each predicted one- and three-year survival and infarct-free survival probabilities for 100 patients. Fifty patients appeared in multiple samples for assessing interphysician variability. Cox regression models, developed using patients not in the test samples, predicted corresponding outcome probabilities for each test patient. Overall, model predictions correlated better with actual patient 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 Cox 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 Cox model was superior to all five doctors. Where predictions were made by multiple doctors, the interphysician variability was substantial. 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.

Full Text

Duke Authors

Cited Authors

  • Lee, KL; Pryor, DB; Harrell, FE; Califf, RM; Behar, VS; Floyd, WL; Morris, JJ; Waugh, RA; Whalen, RE; Rosati, RA

Published Date

  • April 1986

Published In

Volume / Issue

  • 80 / 4

Start / End Page

  • 553 - 560

PubMed ID

  • 3963036

Pubmed Central ID

  • 3963036

Electronic International Standard Serial Number (EISSN)

  • 1555-7162

International Standard Serial Number (ISSN)

  • 0002-9343

Digital Object Identifier (DOI)

  • 10.1016/0002-9343(86)90807-7

Language

  • eng