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Comparison of Traditional Cardiovascular Risk Models and Coronary Atherosclerotic Plaque as Detected by Computed Tomography for Prediction of Acute Coronary Syndrome in Patients With Acute Chest Pain
Academic Emergency Medicine, 08/01/2012
Clinical Article
Ferencik M et al. – Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of acute coronary syndrome (ACS) in patients with acute chest pain. The combined information of risk scores and plaque burden significantly improves the discriminatory capacity for the diagnosis of ACS.
Methods- The study was a subanalysis of the Rule Out Myocardial Infarction Using Computer–Assisted Tomography (ROMICAT) trial—a prospective observational cohort study.
- The authors enrolled patients presenting to the emergency department (ED) with a chief complaint of acute chest pain, inconclusive initial evaluation (negative biomarkers, nondiagnostic electrocardiogram [ECG]), and no history of coronary artery disease (CAD).
- Patients underwent contrast–enhanced 64–multidetector–row cardiac CT and received standard clinical care (serial ECG, cardiac biomarkers, and subsequent diagnostic testing, such as exercise treadmill testing, nuclear stress perfusion imaging, and/or invasive coronary angiography), as deemed clinically appropriate.
- The clinical providers were blinded to CT results.
- The chest pain score was calculated and the results were dichotomized to ≥10 (high–risk) and <10 (low–risk).
- Three risk scores were calculated, Goldman, Sanchis, and Thrombolysis in Myocardial Infarction (TIMI), and each patient was assigned to a low–, intermediate–, or high–risk category.
- Because of the low number of subjects in the high–risk group, the intermediate– and high–risk groups were combined into one.
- CT images were evaluated for the presence of plaque in 17 coronary segments.
- Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS.
- The prognostic discriminatory capacity of the risk scores and plaque burden for ACS was assessed using c–statistics.
- The differences in area under the receiver–operating characteristic curve (AUC) and c–statistics were tested by performing the –2 log likelihood ratio test of nested models.
- A p value <0.05 was considered statistically significant.
- Among 368 subjects, 31 (8%) subjects were diagnosed with ACS.
- Goldman (AUC = 0.61), Sanchis (AUC = 0.71), and TIMI (AUC = 0.63) had modest discriminatory capacity for the diagnosis of ACS.
- Plaque burden was the strongest predictor of ACS (AUC = 0.86; p < 0.05 for all comparisons with individual risk scores).
- The combination of plaque burden and risk scores improved prediction of ACS (plaque + Goldman AUC = 0.88, plaque + Sanchis AUC = 0.90, plaque + TIMI AUC = 0.88; p < 0.01 for all comparisons with coronary plaque burden alone).
