Stress CT Perfusion in Patients With Chest Pain
|First Received Date ICMJE||October 21, 2013|
|Last Updated Date||October 25, 2013|
|Start Date ICMJE||January 2014|
|Estimated Primary Completion Date||December 2016 (final data collection date for primary outcome measure)|
|Current Primary Outcome Measures ICMJE
||Ability to detect stress-induced perfusion abnormalities by 3D analysis of MDCT images [ Time Frame: 6 months ] [ Designated as safety issue: No ]|
|Original Primary Outcome Measures ICMJE||Same as current|
|Change History||Complete list of historical versions of study NCT01969916 on ClinicalTrials.gov Archive Site|
|Current Secondary Outcome Measures ICMJE||Not Provided|
|Original Secondary Outcome Measures ICMJE||Not Provided|
|Current Other Outcome Measures ICMJE||Not Provided|
|Original Other Outcome Measures ICMJE||Not Provided|
|Brief Title ICMJE||Stress CT Perfusion in Patients With Chest Pain|
|Official Title ICMJE||Comprehensive Evaluation of Patients With Chest Pain Using Cardiac Computed Tomography: Value of Adding Regadenoson Stress Perfusion Imaging to Noninvasive Coronary Angiography|
Our hypothesis is that quantitative 3D analysis of cardiac CT images obtained during vasodilator stress can accurately identify patients presenting at the emergency department with acute chest pain due to underlying hemodynamically significant coronary stenosis, aid in the identification of individuals most likely to benefit from revascularization, and thus improve the ability to predict patient outcomes.
Our goals are:
Background. In the United States, more than 8 million patients require emergency department evaluation for acute chest pain every year. The estimated cost for these evaluations is > $10 billion and the loss of economic productivity is likely far greater. Multidetector computed tomography (MDCT) is a popular alternative to diagnostic invasive coronary angiography (ICA) (Deetjen, et al. 2007, Schroeder, et al. 2008, de Roos 2010) and is gaining wide clinical acceptance for its ability to rule out significant coronary artery disease (CAD) (Hulten, Bittencourt, Ghoshhajra and Blankstein 2012). However, in patients with acute chest pain, abnormal CT coronary angiography (CTCA) findings frequently result in additional nuclear myocardial perfusion imaging (MPI) to determine the hemodynamic significance of coronary stenosis (Garcia, Lessick and Hoffmann 2006, Deetjen, et al. 2007, Miller, et al. 2008, Schroeder, et al. 2008, de Roos 2010). This is because the presence and extent of myocardial ischemia is more important than the severity of a coronary stenosis for identifying patients who would benefit from coronary revascularization. Accordingly, with the growing interest in simultaneous evaluation of coronary anatomy and the hemodynamic significance of CAD in a single test, studies have focused on the potential of MDCT to assess myocardial perfusion (Techasith and Cury 2011). One hurdle this approach needs to overcome is that it relies on visual assessment of manually selected 2D slices, rather than 3D analysis of the entire myocardium, and requires manual adjustment of contrast windows, both carrying the risk of missing subendocardial perfusion defects.
Prior data and hypothesis. To overcome these limitations, we recently developed a technique for quantitative 3D analysis of myocardial perfusion, which uses the distribution of x-ray attenuation to calculate for each myocardial segment an index of severity and extent of perfusion abnormality (Kachenoura, et al. 2010). Having confirmed the ability of this analysis to detect regadenoson-induced perfusion abnormalities in consecutive patients referred for CTCA (Mor-Avi, et al. 2012), we propose a new study aimed at determining the value of this methodology in patients presenting in the emergency department with acute chest pain. We selected this cohort to further validate CT perfusion analysis and to determine whether it provides additive utility over CTCA alone, because these patients are increasingly referred by emergency departments for CTCA in large numbers, instead of nuclear vasodilator stress testing, thus losing valuable physiologic information. Because our perfusion index was specifically designed to take into account the fact that stress-induced perfusion defects are subendocardial, we hypothesize that our quantitative 3D analysis can accurately identify patients presenting at the emergency department with acute chest pain due to underlying hemodynamically significant coronary stenosis, aid in the identification of individuals most likely to benefit from revascularization, and thus improve the ability to predict patient outcomes. Indeed, our previous study showed that with regadenoson, our perfusion index is 2-3 times higher in myocardial segments supplied by arteries with significant stenosis (Patel, et al. 2011), and thus improves the diagnosis of hemodynamically significant CAD.
Aims. This study was designed to achieve the following goals: (1) to test the above hypothesis by comparing stress MDCT perfusion data with invasive fractional flow reserve (FFR) data in patients with significant stenosis who undergo ICA; and (2) to determine the added value of MDCT perfusion as an adjunct to CTCA for predicting patient outcomes.
Study design. In this study, MDCT imaging will be performed during regadenoson stress in approximately 300 consecutive patients with acute chest pain referred by the emergency department for CTCA, who agree to undergo additional MDCT imaging during vasodilator stress. Patients with contraindications to CTCA, including known allergies to iodine, renal dysfunction (creatinine >1.6 mg/dL), inability to perform a 10 sec breath-hold, and contraindications to beta-blockers or regadenoson, such as chronic obstructive pulmonary disease, advanced heart block or systolic blood pressure <90 mmHg, will be excluded from the study. In addition, patients with a history of cardiothoracic surgery or pacemaker or coronary stent implantation will be excluded. Myocardial perfusion will be assessed using quantitative volumetric analysis, but will not be reported to the referring physician in order to avoid referral bias. In each patient, we will collect the following information at the time of enrollment: age, gender, height, weight, blood pressure, tobacco use, history of heart disease, hypertension, stroke and diabetes. In addition, a blood sample will be obtained to allow analysis of lipids and cardiac enzymes. In a subgroup of patients who also undergo ICA, MDCT perfusion will be compared with the ICA findings, including the degree of stenosis and FFR. Enrollment will be stopped when 30 patients with ICA data are enrolled (based on power analysis). All study patients will be followed up to determine the predictive value of MDCT perfusion for major cardiovascular events at 1 moth and 1 year after presentation.
MDCT imaging protocol. Beta-blocker metoprolol will be given orally (50 mg, 1 hr prior to imaging) and/or intravenously (5 to 15 mg immediately prior to imaging), as necessary to achieve a target heart rate of <70 bpm. Images will be acquired during suspended respiration (256-channel iCT scanner, Philips). Initially, CTCA will be performed at rest according to the standard clinical protocol. Then, regadenoson (Lexiscan, Astellas) will be administered (0.4mg, i.v. bolus) at least 15 minutes later to ensure contrast clearance. An additional set of images will be acquired 1 minute after the administration of regadenoson to ensure imaging during peak vasodilator effect. Stress images will be acquired following injection of 50 ml of iodinated contrast at a rate of 4 ml/sec, using prospective gating, in order to minimize radiation exposure, resulting in an average of 2 mSv for the additional stress scan.
MDCT image analysis. Myocardial perfusion will be analyzed both at rest and during vasodilator stress. Following manual initialization of endo- and epicardial boundaries in 5 to 6 slices, the endo- and epicardial 3D surfaces will be automatically estimated using the level-set technique (Corsi, et al. 2005). The 3D region of interest confined between the endocardial and epicardial surfaces will be identified as LV myocardium and divided into 3D wedge-shaped myocardial segments, according to standard AHA segmentation (Cerqueira, et al. 2002): 6 basal, 6 mid-ventricular, and 4 apical segments. For each myocardial segment, mean x-ray attenuation value will be automatically measured in Hounsfeld units (HU). Then x-ray attenuation in all LV slices from base to apex will be used as described previously (Kachenoura, et al. 2010) to generate a bull's eye display of myocardial attenuation normalized by mean LV cavity attenuation, resembling MPI bull's eyes. Unlike our previous study of fixed perfusion defects (Kachenoura, et al. 2010), in which the bull's eyes displayed transmural attenuation, in this study we will also create bull's eyes of subendocardial attenuation to optimize the visualization of stress-induced subendocardial perfusion defects. In addition to the bull's eye displays of subendocardial attenuation, this information will be mapped onto the reconstructed 3D-rendered endocardial surface to facilitate the appreciation of the location and extent of perfusion abnormality. This 3D display allows manually rotating the model to allow visualization from any angle, and provides a more realistic view of the hypoenhanced area with less distortion than the bull's eyes. Finally, for each myocardial segment, a quantitative index of extent and severity of perfusion abnormality, Qh, will be calculated as a mathematical product of the number of voxels with low attenuation in percent of the total volume of the segment, reflecting the extent of the defect, and the difference between the attenuation in these voxels and the previously determined normal attenuation in the same anatomic location, reflecting the severity of the defect (Kachenoura, et al. 2010).
Objective detection of perfusion abnormalities. To allow objective detection of perfusion abnormalities, data collected in our earlier pilot study were used to determine the abnormality cutoff values for perfusion index Qh. Myocardial segments were divided into two groups according to CTCA findings: segments supplied by coronary arteries with stenosis located proximally to the specific segment and causing >50% luminal narrowing on CTCA, and segments supplied by arteries without significant stenosis or with stenosis located distally to the segment. Receiver operator curve (ROC) analysis was used to determine optimal abnormality thresholds for index Qh for both rest and stress. We will use these thresholds in the planned prospective study to objectively classify segmental myocardial perfusion as normal or abnormal. Segments with Qh above the corresponding threshold will be considered abnormal. A territory of an individual coronary artery will be considered abnormal when the perfusion index is abnormal in at least one segment. For the patient-by-patient analysis, abnormal perfusion will be diagnosed when at least one territory is abnormal.
Inter-technique comparisons. In order to allow comparisons between MDCT perfusion and ICA findings, coronary anatomy depicted on each patient's MDCT volume rendering of the heart will be used to determine the perfusion territory of each artery and its major branches, i.e. to assign each myocardial segment to the territory of a specific coronary artery. This will allow us to predict normal or abnormal perfusion in each segment based on the ICA determinations of the presence, location and severity of stenosis. Significant stenosis will be defined by a 50% and separately 70% decrease in luminal cross-sectional area. Comparisons with ICA will be performed for perfusion index Qh by itself, and also as an adjunct to CTCA findings, i.e. percent luminal narrowing and FFR. All inter-technique comparisons will be performed on a segment-by-segment, vascular territory and patient-by-patient basis. Inter-technique agreement will be assessed by calculating sensitivity, specificity, positive and negative predictive values (PPV, NPV) and overall accuracy against the respective reference standard. The accuracy of MDCT perfusion combined with CTCA findings will be compared with that of CTCA alone to determine the added clinical value of MDCT perfusion data.
Patient follow-up. Patients enrolled in the study will be followed up for a minimum of 1 year from the stress test for major adverse cardiovascular events, including coronary revascularization, hospitalization for unstable angina, myocardial infarction or death. This information will be obtained from the patients' medical records, Social Security Death Index, and by phone calls to the patients' homes. The collected follow-up data will be used to determine the prevalence of adverse events in patients with coronary stenosis >50% and abnormal perfusion, which will be compared to the remaining patients (chi-square test). The follow-up data will also be used to study the ability of regadenoson-induced perfusion abnormalities detected on MDCT images to predict event-free survival. This will be achieved by univariate logistic regression analysis, followed by multivariate regression for variables found to predict outcomes by the univariate regression. The predictive value of the combination of stenosis on CTCA with perfusion defects will be compared with that of the conventional markers, such as percent coronary luminal narrowing on CTCA alone, cardiac enzymes, and TIMI risk score. Finally, we will determine whether the combination of these conventional markers with the MDCT-derived perfusion data would predict patient outcomes better than either one of them alone.
Anticipated results. We anticipate that perfusion abnormalities detected on MDCT images will correlate with the findings of ICA, and when added to CTCA findings, will improve the accuracy of MDCT evaluation of CAD compared to these reference standards. In addition, we anticipate that when added to the conventional markers, such as cardiac enzymes and TIMI risk score, vasodilator stress induced MDCT perfusion abnormalities will predict adverse cardiovascular events better than these traditional indices alone.
Innovation and significance. To our knowledge, this will be the first study to validate against ICA reference quantitative 3D evaluation of myocardial perfusion from MDCT images obtained during vasodilator stress in patients referred for CTCA evaluation of acute chest pain. These patients are increasingly referred for CTCA in large numbers, instead of nuclear vasodilator stress testing, thus losing valuable physiologic information. Because the addition of stress perfusion will allow elucidating the impact of coronary lesions in the same test, such addition promises to improve the diagnostic performance of cardiac CT in the evaluation of acute chest pain. This methodology may prove as a single-stop alternative to costly serial testing. Moreover, if this comprehensive approach is proven to better predict patient outcomes compared to current clinical algorithms, it may become a new tool for risk stratification and planning individual patient's treatment. We anticipate that the results of our study will support the use of this methodology in every patient with chest pain referred for CTCA, similar to the routine use of vasodilator stress with MPI.
|Study Type ICMJE||Interventional|
|Study Phase||Not Provided|
|Study Design ICMJE||Endpoint Classification: Efficacy Study
Intervention Model: Single Group Assignment
Masking: Open Label
Primary Purpose: Diagnostic
|Condition ICMJE||Coronary Artery Disease|
|Intervention ICMJE||Drug: Regadenoson
Patients will be given a single dose of Lexiscan (0.4 mg, iv bolus)
Other Name: Lexiscan
|Study Arm (s)||Regadenoson
Intervention: Drug: Regadenoson
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
|Recruitment Status ICMJE||Not yet recruiting|
|Estimated Enrollment ICMJE||300|
|Estimated Completion Date||December 2017|
|Estimated Primary Completion Date||December 2016 (final data collection date for primary outcome measure)|
|Eligibility Criteria ICMJE||
|Ages||18 Years and older|
|Accepts Healthy Volunteers||No|
|Location Countries ICMJE||United States|
|NCT Number ICMJE||NCT01969916|
|Other Study ID Numbers ICMJE||15237B-AM005|
|Has Data Monitoring Committee||No|
|Responsible Party||University of Chicago|
|Study Sponsor ICMJE||University of Chicago|
|Collaborators ICMJE||Astellas Pharma Inc|
|Information Provided By||University of Chicago|
|Verification Date||October 2013|
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