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

Frederick Korley, Constantine Gatsonis, Bradley S. Snyder, Richard T. George, Thura Abd, Stefan Zimmerman, Harold I. Litt, Judd E. Hollander

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Cardiovascular Computed Tomography
DOIs
StateAccepted/In press - Sep 23 2016

Keywords

  • Cardiac risk factors
  • Coronary artery disease
  • CT coronary angiography
  • Emergency department
  • Modeling
  • Prediction

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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