Clinical risk factors alone are inadequate for predicting significant coronary artery disease

• Published in: Journal of cardiovascular computed tomography 2017-07, Vol.11 (4), p.309-316
• Main Authors: Korley, Frederick K., MD, PhD, Gatsonis, Constantine, PhD, Snyder, Bradley S., MS, George, Richard T., MD, Abd, Thura, M.D, Zimmerman, Stefan L., MD, Litt, Harold I., MD, PhD, Hollander, Judd E., MD
• Format: Article
• Language: English
• Subjects: Acute Coronary Syndrome - diagnostic imaging; Acute Coronary Syndrome - etiology; Adult; Area Under Curve; Cardiac risk factors; Cardiovascular; Computed Tomography Angiography; Coronary Angiography - methods; Coronary artery disease; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - etiology; CT coronary angiography; Emergency department; Female; Humans; Male; Middle Aged; Modeling; Prediction; Predictive Value of Tests; Prognosis; Randomized Controlled Trials as Topic; Reproducibility of Results; Risk Assessment; Risk Factors; ROC Curve; Severity of Illness Index
• ISSN: 1934-5925; 1876-861X
• DOI: 10.1016/j.jcct.2017.04.011

Abstract Objective We sought to derive and validate a model for identifying suspected ACS patients harboring undiagnosed significant coronary artery disease (CAD). Methods This was a secondary analysis of data from a randomized control trial (RCT). Patients randomized to the CTA arm of an RCT examining a CTA-based strategy for ruling-out acute coronary syndrome (ACS) constitute the derivation cohort, which was randomly divided into a training dataset (2/3, used for model derivation) and a test dataset (1/3, used for internal validation (IV)). ED patients from a different center receiving CTA to evaluate for suspected ACS constitute the external validation (EV) cohort. Primary outcome was CTA-assessed significant CAD (stenosis of ≥50% in a major coronary artery). Results In the derivation cohort, 11.2% (76/679) of subjects had CTA-assessed significant CAD, and in the EV cohort, 8.2% of subjects (87/1056) had CTA-assessed significant CAD. Age was the strongest predictor of significant CAD among the clinical risk factors examined. Predictor variables included in the derived logistic regression model were: age, sex, tobacco use, diabetes, and race. This model exhibited an area under the receiver operating characteristic curve (ROC AUC) of 0.72 (95% CI: 0.61–0.83) based on IV, and 0.76 (95% CI: 0.70, 0.82) based on EV. The derived random forest model based on clinical risk factors yielded improved but not sufficient discrimination of significant CAD (ROC AUC = 0.76 [95% CI: 0.67–0.85] based on IV). Coronary artery calcium score was a more accurate predictor of significant CAD than any combination of clinical risk factors (ROC AUC = 0.85 [95% CI: 0.76–0.94] based on IV; ROC AUC = 0.92 [95% CI: 0.88–0.95] based on EV). Conclusions Clinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS patients harboring undiagnosed significant coronary artery disease.