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Ann Thorac Surg 2004;78:1868-1877
© 2004 The Society of Thoracic Surgeons
a Lahey Clinic, Burlington, Massachusetts, USA
b Cleveland Clinic Foundation, Cleveland, Ohio, USA
c University of Florida, Jacksonville, Florida, USA
d University of Colorado HSC, Denver, Colorado, USA
e Providence Health System, Portland, Oregon, USA
f University of Alabama, Birmingham, Alabama, USA
g Papworth Hospital, Cambridge, United Kingdom
h Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
i Duke Clinical Research Institute, Durham, North Carolina, USA
* Address reprint requests to Dr Shahian, Department of Thoracic and Cardiovascular Surgery, Lahey Clinic, 41 Mall Rd, Burlington, MA 01805 (E-mail: david.m.shahian{at}lahey.org).
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.
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