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 2012; 19:934–942 © 2012 by the Society for Academic Emergency Medicine Objectives: The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (...
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| Published in: | Academic emergency medicine Vol. 19; no. 8; pp. 934 - 942 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford, UK
Blackwell Publishing Ltd
01-08-2012
Wiley Subscription Services, Inc |
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| Abstract | ACADEMIC EMERGENCY MEDICINE 2012; 19:934–942 © 2012 by the Society for Academic Emergency Medicine
Objectives: The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved 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. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. 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.
Results: 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).
Conclusions: Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of 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. |
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| AbstractList | ACADEMIC EMERGENCY MEDICINE 2012; 19:934–942 © 2012 by the Society for Academic Emergency Medicine
Objectives: The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved 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. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. 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.
Results: 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).
Conclusions: Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of 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. The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved the discriminatory capacity for the diagnosis of ACS. 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. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. 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). Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of 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. OBJECTIVESThe objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved the discriminatory capacity for the diagnosis of ACS. METHODSThe 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. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. 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. RESULTSAmong 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). CONCLUSIONSRisk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of 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. The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved the discriminatory capacity for the diagnosis of ACS. 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. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. 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). Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of 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. |
| Author | Seneviratne, Sujith Rogers, Ian S. Ferencik, Maros Pena, Antonio J. Hoffmann, Udo Parry, Blair Alden Nichols, John H. Brady, Thomas J. Shapiro, Michael D. Truong, Quynh A. Brown, David F. Schlett, Christopher L. Bamberg, Fabian Cury, Ricardo C. Nagurney, John T. |
| Author_xml | – sequence: 1 givenname: Maros surname: Ferencik fullname: Ferencik, Maros organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 2 givenname: Christopher L. surname: Schlett fullname: Schlett, Christopher L. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 3 givenname: Fabian surname: Bamberg fullname: Bamberg, Fabian organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 4 givenname: Quynh A. surname: Truong fullname: Truong, Quynh A. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 5 givenname: John H. surname: Nichols fullname: Nichols, John H. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 6 givenname: Antonio J. surname: Pena fullname: Pena, Antonio J. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 7 givenname: Michael D. surname: Shapiro fullname: Shapiro, Michael D. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 8 givenname: Ian S. surname: Rogers fullname: Rogers, Ian S. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 9 givenname: Sujith surname: Seneviratne fullname: Seneviratne, Sujith organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 10 givenname: Blair Alden surname: Parry fullname: Parry, Blair Alden organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 11 givenname: Ricardo C. surname: Cury fullname: Cury, Ricardo C. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 12 givenname: Thomas J. surname: Brady fullname: Brady, Thomas J. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 13 givenname: David F. surname: Brown fullname: Brown, David F. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 14 givenname: John T. surname: Nagurney fullname: Nagurney, John T. organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA – sequence: 15 givenname: Udo surname: Hoffmann fullname: Hoffmann, Udo organization: From the Cardiology Division (MF, QAT), the Department of Radiology (CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), the Department of Emergency Medicine (BAP, DFB, JTN), and the Cardiac MR/PET/CT Program (MF, CLS, FB, QAT, JHN, AJP, MDS, ISR, SS, RCC, TJB, UH), Massachusetts General Hospital and Harvard Medical School, Boston, MA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22849339$$D View this record in MEDLINE/PubMed |
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| Copyright | 2012 by the Society for Academic Emergency Medicine 2012 by the Society for Academic Emergency Medicine. Copyright Hanley & Belfus, Inc. Aug 2012 2012 by the Society for Academic Emergency Medicine 2012 |
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| Notes | istex:E47E20B8B1DC48FAC4A01F70C89E79CEEF52C7B0 ark:/67375/WNG-XFL2325Z-V ArticleID:ACEM1417 Drs. Ferencik, Rogers, Truong, and Shapiro were supported by the National Institutes of Health grant (T32 HL076136). Dr. Hoffmann has received research grants from Siemens Medical Solutions and General Electric Healthcare. Dr. Nagurney is funded by Biosite for a biomarker research study. The rest of the authors have no disclosures or potential conflicts of interest to report. Supervising Editor: Sarah A. Stahmer, MD. This work was supported by the National Institutes of Health (RO1 HL080053) and in part supported by Siemens Medical Solutions and General Electrics Healthcare. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| Snippet | ACADEMIC EMERGENCY MEDICINE 2012; 19:934–942 © 2012 by the Society for Academic Emergency Medicine
Objectives: The objective was to determine the association... The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome... OBJECTIVESThe objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with... |
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| SubjectTerms | Acute Coronary Syndrome - complications Acute Coronary Syndrome - diagnosis Acute Coronary Syndrome - diagnostic imaging Acute coronary syndromes Acute Pain Atherosclerosis Chest Pain - etiology Coronary Artery Disease - diagnostic imaging Emergency medical care Female Humans Male Medical diagnosis Middle Aged Pain Plaque, Atherosclerotic - diagnostic imaging Prospective Studies Risk Assessment - methods Risk Factors Tomography Tomography, X-Ray Computed |
| Title | 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 |
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