Cardiac surgery risk models: a position article.
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.
Shahian
David M
DM
Lahey Clinic, Burlington, Massachusetts 01805, USA. david.m.shahian@lahey.org
Blackstone
Eugene H
EH
Edwards
Fred H
FH
Grover
Frederick L
FL
Grunkemeier
Gary L
GL
Naftel
David C
DC
Nashef
Samer A M
SA
Nugent
William C
WC
Peterson
Eric D
ED
D001499:Bayes Theorem; D006348:Cardiac Surgical Procedures / Q000706:statistics & numerical data*; D001026:Coronary Artery Bypass / Q000706:statistics & numerical data; D016208:Databases, Factual; D003953:Diagnosis-Related Groups; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008955:Models, Cardiovascular; D016017:Odds Ratio; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012307:Risk Factors; D013318:Stroke Volume; D016896:Treatment Outcome