PMID: 8613603Mar 15, 1996Paper

Development and validation of a Bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates

Journal of the American College of Cardiology
G J L'ItalienK A Eagle

Abstract

This study sought to develop and validate a Bayesian risk prediction model for vascular surgery candidates. Patients who require surgical treatment of peripheral vascular disease are at increased risk of perioperative cardiac morbidity and mortality. Existing prediction models tend to underestimate risk in vascular surgery candidates. The cohort comprised 1,081 consecutive vascular surgery candidates at five medical centers. Of these, 567 patients from two centers ("training" set) were used to develop the model, and 514 patients from three centers were used to validate it ("validation" set). Risk scores were developed using logistic regression for clinical variables: advanced age (>70 years), angina, history of myocardial infarction, diabetes mellitus, history of congestive heart failure and prior coronary revascularization. A second model was developed from dipyridamole-thallium predictors of myocardial infarction (i.e., fixed and reversible myocardial defects and ST changes). Model performance was assessed by comparing observed event rates with risk estimates and by performing receiver-operating characteristic curve (ROC) analysis. The postoperative cardiac event rate was 8% for both sets. Prognostic accuracy (i.e., ROC area)...Continue Reading

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