Background. Cardiac surgical databases are typically used to compare performance rather than to predict performance. Application of analytic: techniques to representative clinical data allows for the creation of highly reliable prediction models for outcomes such as mortality after coronary artery bypass grafting. Methods. The Northern New England Cardiovascular Disease Study Group collects clinical data from the six regional cardiac institutions. The four basic components of the one-page clinical data form ale demographic information, comorbidity data, severity of illness data, and outcomes. From these, a mathematical model predicts the likelihood of inhospital mortality after coronary artery bypass grafting, Results. The Northern New England Cardiovascular Disease Study Group mortality prediction rule was validated and bound to predict and discriminate well, It is updated yearly based on the most current data. At Dartmouth-Hitchcock Medical Center, the rule has been combined with other decision-support tools based on American College of Cardiology/American Heart Association indications for coronary artery bypass grafting. These data are compared with data supplied by patients on their expectations for operation and their assessments of acceptable mortality risk. By expanding the database to include variables such as Ei-year survival and additional outcomes, and additional interventions, an "electronic second opinion" is made available to clinicians. Conclusions. Although experience with decision-support tools has been positive, prediction rules have certain limitations. Their accuracy depends on data that are consistently tracked; thus, transfer to other patient populations must be approached with caution. The challenge for the future is to develop and validate similar techniques that apply to more difficult clinical situations. (C) 1997 by The Society of Thoracic Surgeons.
