학술논문

A proposed clinical model for efficient utilization of invasive coronary angiography.
Document Type
Academic Journal
Author
Taylor CM; University of British Columbia, Vancouver, British Columbia, Canada. cmtaylor@providencehealth.bc.ca; Humphries KHPu AGhali WGao MKnudtson MHoffmann UCarere RG
Source
Publisher: Excerpta Medica Country of Publication: United States NLM ID: 0207277 Publication Model: Print Cited Medium: Internet ISSN: 1879-1913 (Electronic) Linking ISSN: 00029149 NLM ISO Abbreviation: Am J Cardiol Subsets: MEDLINE
Subject
Language
English
Abstract
More than 1/4 of patients who undergo invasive coronary angiography are found to have no visible or nonobstructive (<50% stenosis) coronary artery disease (CAD). With the rapid evolution of noninvasive imaging for CAD diagnosis, avoiding invasive coronary angiography in patients unlikely to require coronary revascularization is desirable. We undertook to develop a clinical prediction tool to identify patients with a low likelihood of obstructive (> or =50% stenosis) CAD. The derivation cohort included 24,637 patients with a diagnosis of "stable angina" or "acute coronary syndrome" referred for first cardiac catheterization in the province of British Columbia, Canada. The model was validated using an external dataset from the province of Alberta and comprised 18,606 patients. Seven variables (female gender, age <50 years, atypical Canadian Cardiovascular Society angina class, absence of ST-segment change on electrocardiogram, lifelong nonsmoking, and absence of diabetes and hyperlipidemia) were associated with the angiographic finding of "no or nonobstructive CAD." The c-statistics for the derivation model were 0.76 and 0.74 using the validation dataset. In conclusion, this simple clinical prediction tool, applied to patients for whom determination of coronary anatomy was clinically indicated, identifies patients who have a low likelihood of obstructive CAD. The patient population identified by this tool may represent a population best suited to a noninvasive diagnostic strategy.