Damini Dey

Pericardial Fat Burden on ECG-Gated Noncontrast CT in Asymptomatic Patients Who Subsequently Experience Adverse Cardiovascular Events

OBJECTIVES We aimed to evaluate whether pericardial fat has value in predicting the risk of future adverse cardiovascular outcomes. BACKGROUND Pericardial fat volume (PFV) and thoracic fat volume (TFV) can be routinely measured from noncontrast computed tomography (NCT) performed for calculating coronary calcium score (CCS) and may predict major adverse cardiac event (MACE) risk. METHODS From a registry of 2,751 asymptomatic patients without known cardiac artery disease and 4-year follow-up for MACE (cardiac death, myocardial infarction, stroke, late revascularization) after NCT, we compared 58 patients with MACE with 174 same-sex, event-free control subjects matched by a propensity score to account for age, risk factors, and CCS. The TFV was automatically calculated, and PFV was calculated with manual assistance in defining the pericardial contour, within which fat voxels were automatically identified. Independent relationships of PFV and TFV to MACE were evaluated using conditional multivariable logistic regression. RESULTS Patients experiencing MACE had higher mean PFV (101.8 +/- 49.2 cm(3) vs. 84.9 +/- 37.7 cm(3), p = 0.007) and TFV (204.7 +/- 90.3 cm(3) vs. 177 +/- 80.3 cm(3), p = 0.029) and higher frequencies of PFV >125 cm(3) (33% vs. 14%, p = 0.002) and TFV >250 cm(3) (31% vs. 17%, p = 0.025). After adjustment for Framingham risk score (FRS), CCS, and body mass index, PFV and TFV were significantly associated with MACE (odds ratio [OR]: 1.74, 95% confidence interval [CI]: 1.03 to 2.95 for each doubling of PFV; OR: 1.78, 95% CI: 1.01 to 3.14 for TFV). The area under the curve from receiver-operator characteristic analyses showed a trend of improved MACE prediction when PFV was added to FRS and CCS (0.73 vs. 0.68, p = 0.058). Addition of PFV, but not TFV, to FRS and CCS improved estimated specificity (0.72 vs. 0.66, p = 0.008) and overall accuracy (0.70 vs. 0.65, p = 0.009) in predicting MACE. CONCLUSIONS Asymptomatic patients who experience MACE exhibit greater PFV on pre-MACE NCT when they are compared with event-free control subjects with similar cardiovascular risk profiles. Our preliminary findings suggest that PFV may help improve prediction of MACE.

Increased volume of epicardial fat is an independent risk factor for accelerated progression of sub-clinical coronary atherosclerosis

BACKGROUND Epicardial adipose tissue (EAT), a metabolically active visceral fat depot surrounding the heart, has been implicated in the pathogenesis of coronary artery disease (CAD) through possible paracrine interaction with the coronary arteries. We examined the association of EAT with metabolic syndrome and the prevalence and progression of coronary artery calcium (CAC) burden. METHODS CAC scan was performed in 333 asymptomatic diabetic patients without prior history of CAD (median age 54 years, 62% males), followed by a repeat scan after 2.7±0.3 years. CAC progression was defined as >2.5mm(3) increase in square root transformed volumetric CAC scores. EAT and intra-thoracic fat volumes were quantified using a dedicated software (QFAT), and were examined in relation to the metabolic syndrome, baseline CAC scores and CAC progression. RESULTS Both epicardial and intra-thoracic fat were associated with metabolic syndrome after adjustment for conventional cardiovascular risk factors, but the association was attenuated after additional adjustment for body mass index. EAT, but not intra-thoracic fat, showed significant association with baseline CAC scores (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.04-1.22, p=0.04) and CAC progression (OR 1.12, 95% CI 1.05-1.19, p<0.001) after adjustment for conventional measures of obesity and risk factors. CONCLUSION EAT volume measured on non-contrast CT is an independent marker for the presence and severity of coronary calcium burden and also identifies individuals at increased risk of CAC progression. EAT quantification may thus add to the prognostic value of CAC imaging.

Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US.

PURPOSE To determine the accuracy of a previously developed automated algorithm (AUTOPLAQ [APQ]) for rapid volumetric quantification of noncalcified and calcified plaque from coronary computed tomographic (CT) angiography in comparison with intravascular ultrasonography (US). MATERIALS AND METHODS This study was approved by the institutional review board and was HIPAA compliant; all patients provided written informed consent. APQ combines derived scan-specific attenuation threshold levels for lumen, plaque, and knowledge-based segmentation of coronary arteries for quantification of plaque components. APQ was validated with retrospective analysis of 22 coronary atherosclerotic plaques in 20 patients imaged with coronary CT angiography and intravascular US within 2 days of each other. Coronary CT angiographic data were acquired by using dual-source CT. For each patient, well-defined plaques without calcifications were selected, and plaque volume was measured with APQ and manual tracing at CT and with intravascular US. Measurements were compared with paired t test, correlation, and Bland-Altman analysis. RESULTS There was excellent correlation between noncalcified plaque volumes quantified with APQ and intravascular US (r = 0.94, P < .001), with no significant differences (P = .08). Mean plaque volume with intravascular US was 105.9 mm³ ± 83.5 (standard deviation) and with APQ was 116.6 mm³ ± 80.1. Mean plaque volume with manual tracing from CT was 100.8 mm³ ± 81.7 and with APQ was 116.6 mm³ ± 80.1, with excellent correlation (r = 0.92, P < .001) and no significant differences (P = .23). CONCLUSION Automated scan-specific threshold level-based quantification of plaque components from coronary CT angiography allows rapid, accurate measurement of noncalcified plaque volumes, compared with intravascular US, and requires a fraction of the time needed for manual analysis.

Increased Pericardial Fat Volume Measured From Noncontrast CT Predicts Myocardial Ischemia by SPECT

OBJECTIVES We evaluated the association between pericardial fat and myocardial ischemia for risk stratification. BACKGROUND Pericardial fat volume (PFV) and thoracic fat volume (TFV) measured from noncontrast computed tomography (CT) performed for calculating coronary calcium score (CCS) are associated with increased CCS and risk for major adverse cardiovascular events. METHODS From a cohort of 1,777 consecutive patients without previously known coronary artery disease (CAD) with noncontrast CT performed within 6 months of single photon emission computed tomography (SPECT), we compared 73 patients with ischemia by SPECT (cases) with 146 patients with normal SPECT (controls) matched by age, gender, CCS category, and symptoms and risk factors for CAD. TFV was automatically measured. Pericardial contours were manually defined within which fat voxels were automatically identified to compute PFV. Computer-assisted visual interpretation of SPECT was performed using standard 17-segment and 5-point score model; perfusion defect was quantified as summed stress score (SSS) and summed rest score (SRS). Ischemia was defined by: SSS - SRS ≥4. Independent relationships of PFV and TFV to ischemia were examined. RESULTS Cases had higher mean PFV (99.1 ± 42.9 cm(3) vs. 80.1 ± 31.8 cm(3), p = 0.0003) and TFV (196.1 ± 82.7 cm(3) vs. 160.8 ± 72.1 cm(3), p = 0.001) and higher frequencies of PFV >125 cm(3) (22% vs. 8%, p = 0.004) and TFV >200 cm(3) (40% vs. 19%, p = 0.001) than controls. After adjustment for CCS, PFV and TFV remained the strongest predictors of ischemia (odds ratio [OR]: 2.91, 95% confidence interval [CI]: 1.53 to 5.52, p = 0.001 for each doubling of PFV; OR: 2.64, 95% CI: 1.48 to 4.72, p = 0.001 for TFV). Receiver operating characteristic analysis showed that prediction of ischemia, as indicated by receiver-operator characteristic area under the curve, improved significantly when PFV or TFV was added to CCS (0.75 vs. 0.68, p = 0.04 for both). CONCLUSIONS Pericardial fat was significantly associated with myocardial ischemia in patients without known CAD and may help improve risk assessment.

Computer-aided Non-contrast CT-based Quantification of Pericardial and Thoracic Fat and Their Associations with Coronary Calcium and Metabolic Syndrome

INTRODUCTION Pericardial fat is emerging as an important parameter for cardiovascular risk stratification. We extended previously developed quantitation of thoracic fat volume (TFV) from non-contrast coronary calcium (CC) CT scans to also quantify pericardial fat volume (PFV) and investigated the associations of PFV and TFV with CC and the Metabolic Syndrome (METS). METHODS TFV is quantified automatically from user-defined range of CT slices covering the heart. Pericardial fat contours are generated by spline interpolation between 5-7 control points, placed manually on the pericardium within this cardiac range. Contiguous fat voxels within the pericardium are identified as pericardial fat. PFV and TFV were measured from non-contrast CT for 201 patients. In 105 patients, abdominal visceral fat area (VFA) was measured from an additional single-slice CT. In 26 patients, images were quantified by two readers to establish inter-observer variability. TFV and PFV were examined in relation to Body Mass Index (BMI), waist circumference and VFA, standard coronary risk factors (RF), CC (Agatston score >0) and METS. RESULTS PFV and TFV showed excellent correlation with VFA (R=0.79, R=0.89, p<0.0001), and moderate correlation with BMI (R=0.49, R=0.48, p<0.0001). In 26 scans, the inter-observer variability was greater for PFV (8.0+/-5.3%) than for TFV (4.4+/-3.9%, p=0.001). PFV and TFV, but not RF, were associated with CC [PFV: p=0.04, Odds Ratio 3.1; TFV: p<0.001, OR 7.9]. PFV and TFV were also associated with METS [PFV: p<0.001, OR 6.1; TFV p<0.001, OR 5.7], unlike CC [OR=1.0 p=NS] or RF. PFV correlated with low-HDL and high-glucose; TFV correlated with low-HDL, low-adiponectin, and high glucose and triglyceride levels. CONCLUSIONS PFV and TFV can be obtained easily and reproducibly from routine CC scoring scans, and may be important for risk stratification and monitoring.

Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions

Abstract Aims Coronary plaque characteristics are associated with ischaemia. Differences in plaque volumes and composition may explain the discordance between coronary stenosis severity and ischaemia. We evaluated the association between coronary stenosis severity, plaque characteristics, coronary computed tomography angiography (CTA)-derived fractional flow reserve (FFRCT), and lesion-specific ischaemia identified by FFR in a substudy of the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). Methods and results Coronary CTA stenosis, plaque volumes, FFRCT, and FFR were assessed in 484 vessels from 254 patients. Stenosis >50% was considered obstructive. Plaque volumes (non-calcified plaque [NCP], low-density NCP [LD-NCP], and calcified plaque [CP]) were quantified using semi-automated software. Optimal thresholds of quantitative plaque variables were defined by area under the receiver-operating characteristics curve (AUC) analysis. Ischaemia was defined by FFR or FFRCT ≤0.80. Plaque volumes were inversely related to FFR irrespective of stenosis severity. Relative risk (95% confidence interval) for prediction of ischaemia for stenosis >50%, NCP ≥185 mm3, LD-NCP ≥30 mm3, CP ≥9 mm3, and FFRCT ≤0.80 were 5.0 (3.0–8.3), 3.7 (2.4–5.6), 4.6 (2.9–7.4), 1.4 (1.0–2.0), and 13.6 (8.4–21.9), respectively. Low-density NCP predicted ischaemia independent of other plaque characteristics. Low-density NCP and FFRCT yielded diagnostic improvement over stenosis assessment with AUCs increasing from 0.71 by stenosis >50% to 0.79 and 0.90 when adding LD-NCP ≥30 mm3 and LD-NCP ≥30 mm3 + FFRCT ≤0.80, respectively. Conclusion Stenosis severity, plaque characteristics, and FFRCT predict lesion-specific ischaemia. Plaque assessment and FFRCT provide improved discrimination of ischaemia compared with stenosis assessment alone.

Moving beyond binary grading of coronary arterial stenoses on coronary computed tomographic angiography: insights for the imager and referring clinician.

OBJECTIVES We evaluated the technical and clinical utility of visual 5-point coronary stenosis grading on coronary computed tomographic angiography (CCTA). BACKGROUND The binary approach used to assess coronary stenoses on CCTA does not adequately describe borderline obstructive lesions and limits full expression of clinically useful information. METHODS From 84 patients who underwent CCTA and invasive angiography, we identified 278 native coronary segments with > or =25% stenosis on CCTA after excluding all <25% stenotic, stented, and uninterpretable segments. Fifty <25% stenotic segments were randomly selected as controls. Segmental stenosis severity on CCTA was consensually graded using a 0 to 5 scale (grade 0 = none, grade 1 = 1% to 24%, grade 2 = 25% to 49%, grade 3 = 50% to 69%, grade 4 = 70% to 89%, grade 5 = 90% to 100%) by 2 readers, using visual inspection and computed tomography-based quantification (CTQCA). Invasive angiography-based stenosis quantification (IQCA) was performed for all segments, using the same 0 to 5 scale to score stenosis severity. RESULTS On CCTA, 185 (56%) segments had intermediate stenoses (grade 2 or grade 3). Stenosis severity by IQCA increased significantly with each step-up in CCTA grade (p < 0.001). CTQCA did not perform better than visual inspection. Visual CCTA stenosis grading differed from IQCA by >1 grade in only 4% of grade 2 to grade 5 segments (10 of 278; 2% of CCTA grade 2 segments, 4% of grade 3, 8% of grade 4, 2% of grade 5). Overall quantitative correlation was strong (r = 0.82) with high variability in agreement between CTQCA and IQCA for individual segments (95% of differences between 27.2% and 34.6%). CONCLUSIONS With current CCTA technology, experienced readers should consider adopting a visually based, multitiered grading approach to evaluate coronary stenoses. A < or =49% lesion on CCTA can be considered virtually exclusive of > or =70% stenosis by invasive angiography.

Increase in epicardial fat volume is associated with greater coronary artery calcification progression in subjects at intermediate risk by coronary calcium score: A serial study using non-contrast cardiac CT

OBJECTIVE Epicardial fat volume (EFV) is related to calcified coronary plaques. However, it is unknown whether baseline EFV or changes in EFV affect the progression of coronary artery calcification over time. METHODS We identified 375 consecutive asymptomatic subjects with an intermediate risk of developing coronary artery disease, who underwent serial non-contrast CT at least 3-5 years apart. Subjects were divided into tertiles of CCS progression (% increase) between the 2 scans. Subjects from the upper tertile (High Progressors) were matched by age and gender to 81 subjects from the lower tertile (Low Progressors). All subjects underwent serial measurements of CCS and EFV. Relationships between EFV and CCS progression, and change in plaque number were examined. RESULTS At baseline, there was no difference in EFV, and EFV indexed to body surface area (EFVi) between the groups. At follow-up, EFV, EFVi and percent increase in EFVi-change were higher in High Progressors than Low Progressors (EFV, 102 ± 38 cm(3) vs. 90 ± 35 cm(3), p=0.03; EFVi, 50 ± 16cm(3)/m(2) vs. 46 ± 15 cm(3)/m(2), p=0.03; percent increase in EFVi-change, 15 ± 22% vs. 7 ± 20%, p=0.02). On multivariate analysis, after adjusting for conventional risk factors, EFVi increase ≥15% [odds ratio (OR) 2.3, p<0.05], log (baseline CCS) [OR 0.3, p<0.0001] and scan interval time [p=0.003, OR 1.0] were predictive of being a High Progressor. EFVi increase ≥ 15% (β=3.0, p=0.02) and hypertension (β=3.1, p=0.01) were independent predictors of number of new calcified plaques on follow-up. CONCLUSION Increase in EFV is associated with greater progression of coronary artery calcification in intermediate-risk subjects.

Coronary Arterial 18F-FDG Uptake by Fusion of PET and Coronary CT Angiography at Sites of Percutaneous Stenting for Acute Myocardial Infarction and Stable Coronary Artery Disease

Whether 18F-FDG PET can detect inflammation in the coronary arteries remains controversial. We examined 18F-FDG uptake at the culprit sites of acute myocardial infarction (AMI) after percutaneous coronary stenting (PCS) by coregistering PET and coronary CT angiography (CTA). Methods: Twenty nondiabetic patients with AMI (median age, 62 y; 16 men and 4 women) and 7 nondiabetic patients with stable coronary artery disease (CAD; median age, 67 y; 4 men and 3 women) underwent 18F-FDG PET and coronary CTA 1–6 d after PCS of culprit stenoses. After a low-carbohydrate dietary preparation and more than 12 h of fasting, 480 MBq of 18F-FDG were injected, and PET images were acquired 3 h later. Helical CTA was performed on a dual-source scanner. Stent position on attenuation-correction noncontrast CT and CTA was used to fuse PET and CTA. Two experienced readers masked to patient data independently quantified maximum target-to-background ratio (maxTBR) at each PCS site. A maxTBR greater than 2.0 was the criterion for significant uptake. Results: Compared with stable CAD patients, more AMI patients exhibited a PCS site maxTBR greater than 2.0 (12/20 vs. 1/7, P = 0.04). More AMI patients were active smokers (9/20 vs. 0/7 in stable CAD, P = 0.03). After adjusting for baseline demographic differences, stent–myocardium distance, and myocardial 18F-FDG uptake, presentation of AMI was positively associated with a PCS site maxTBR greater than 2.0 (odds ratio, 31.6; P = 0.044). Prevalence of excess myocardial 18F-FDG uptake was similar in both populations (8/20 AMI vs. 3/7 stable CAD, P = 0.89). Conclusion: Systematic fusion of 18F-FDG PET and coronary CTA demonstrated increased culprit site 18F-FDG uptake more commonly in patients with AMI than in patients with stable CAD. However, this approach failed to detect increased signal at the culprit site in nearly half of AMI patients, highlighting the challenging nature of in vivo coronary artery plaque metabolic imaging. Nonetheless, our findings suggest that imaging of coronary artery inflammation is feasible, and further work evaluating 18F-FDG uptake in high-risk coronary plaques prior to rupture would be of great interest.

Comparison of the Extent and Severity of Myocardial Perfusion Defects Measured by CT Coronary Angiography and SPECT Myocardial Perfusion Imaging

OBJECTIVES We compared electrocardiogram-gated computed tomography (CT) myocardial perfusion imaging (MPI) based on quantification of the extent and severity of perfusion abnormalities to that measured with single-photon emission computed tomography (SPECT) MPI. BACKGROUND Contrast-enhanced CT-MPI has been used for the identification of myocardial ischemia. METHODS We performed CT-MPI during intravenous adenosine infusion in 30 patients with perfusion abnormalities on rest/adenosine stress SPECT-MPI acquired within 60 days (18 stress-rest CT-MPI and 12 stress CT-MPI only). The extent and severity of perfusion defects on SPECT-MPI were assessed on a 5-point scale in a standard 17-segment model, and total perfusion deficit (TPD) was quantified by automated software. The extent and severity of perfusion defects on CT-MPI was visually assessed by 2 observers using the same grading scale and expressed as summed stress score and summed rest score; visually quantified TPD was given by summed stress score/(maximal score of 68) and summed rest score/68. The magnitude of perfusion abnormality on CT-MPI in regions of the myocardium was defined. RESULTS On a per-segment basis, there was good agreement between CT-MPI and SPECT-MPI with a kappa of 0.71 (p < 0.0001) for detection of stress perfusion abnormalities. Automated TPD on SPECT-MPI was similar to visual TPD from CT-MPI (p = 0.65 stress TPD, and p = 0.12 ischemic TPD stress-rest) with excellent agreement (bias = -0.3 for stress TPD, and bias = 1.2 for ischemic TPD) on Bland-Altman analysis. Software-based quantification of the magnitude of stress perfusion deficit and ischemia on CT-MPI were similar to that for automated TPD measured by SPECT (p = 0.88 stress, and p = 0.48 ischemia), with minimal bias (bias = 0.6, and bias = 1.2). CONCLUSIONS Stress and reversible myocardial perfusion deficit measured by CT-MPI using a visual semiquantitative approach and a visually guided software-based approach show strong similarity with SPECT-MPI, suggesting that CT-MPI-based assessment of myocardial perfusion defects may be of clinical and prognostic value.