Cardiac surgery risk models: A position article

David M. Shahian, Eugene H. Blackstone, Fred H. Edwards, Frederick L. Grover, Gary L. Grunkemeier, David C. Naftel, Samer A.M. Nashef, William C. Nugent, Eric D. Peterson

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1868-1877
Number of pages10
JournalAnnals of Thoracic Surgery
Volume78
Issue number5
DOIs
StatePublished - Nov 2004
Externally publishedYes

ASJC Scopus subject areas

  • Surgery
  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine

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