Cardiac surgery risk models: a position article.


Journal Article (Review)

Differences in medical outcomes may result from disease severity, treatment effectiveness, or chance. Because most outcome studies are observational rather than randomized, risk adjustment is necessary to account for case mix. This has usually been accomplished through the use of standard logistic regression models, although Bayesian models, hierarchical linear models, and machine-learning techniques such as neural networks have also been used. Many factors are essential to insuring the accuracy and usefulness of such models, including selection of an appropriate clinical database, inclusion of critical core variables, precise definitions for predictor variables and endpoints, proper model development, validation, and audit. Risk models may be used to assess the impact of specific predictors on outcome, to aid in patient counseling and treatment selection, to profile provider quality, and to serve as the basis of continuous quality improvement activities.

Full Text

Duke Authors

Cited Authors

  • Shahian, DM; Blackstone, EH; Edwards, FH; Grover, FL; Grunkemeier, GL; Naftel, DC; Nashef, SAM; Nugent, WC; Peterson, ED; STS workforce on evidence-based surgery,

Published Date

  • November 2004

Published In

Volume / Issue

  • 78 / 5

Start / End Page

  • 1868 - 1877

PubMed ID

  • 15511504

Pubmed Central ID

  • 15511504

Electronic International Standard Serial Number (EISSN)

  • 1552-6259

Digital Object Identifier (DOI)

  • 10.1016/j.athoracsur.2004.05.054


  • eng

Conference Location

  • Netherlands