TY - JOUR
T1 - Clinical risk factors alone are inadequate for predicting significant coronary artery disease
AU - Korley, Frederick K.
AU - Gatsonis, Constantine
AU - Snyder, Bradley S.
AU - George, Richard T.
AU - Abd, Thura
AU - Zimmerman, Stefan L.
AU - Litt, Harold I.
AU - Hollander, Judd E.
N1 - Publisher Copyright:
© 2017 Society of Cardiovascular Computed Tomography
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
KW - CT coronary angiography
KW - Cardiac risk factors
KW - Coronary artery disease
KW - Emergency department
KW - Modeling
KW - Prediction
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U2 - 10.1016/j.jcct.2017.04.011
DO - 10.1016/j.jcct.2017.04.011
M3 - Article
C2 - 28487137
AN - SCOPUS:85018372795
SN - 1934-5925
VL - 11
SP - 309
EP - 316
JO - Journal of cardiovascular computed tomography
JF - Journal of cardiovascular computed tomography
IS - 4
ER -