Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT

Abstract

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.

Figure 1. Measurement of PFV and TFV From Noncontrast CT

Pericardial and thoracic fat quantification from a noncontrast computed tomography (CT) for a 65-year-old male patient. (A) Shows an axial slice with a clearly visible pericardium. This is manually delineated by the reader (blue outline) as shown in (B), and software-based quantification of pericardial and extrapericardial thoracic fat represented by red and yellow regions, respectively, is shown in (C). PFV = pericardial fat volume; TFV = thoracic fat volume.

Figure 2. Representative Example of PFV, TFV, and Myocardial Ischemia in a Matched Case-Control Pair

Association of increased pericardial fat and thoracic fat with ischemia. The top and bottom panels show (from left to right) axial, coronal, and sagittal sections of a noncontrast CT with pericardial fat in red and thoracic extrapericardial fat in yellow. The last 2 panels on the right show a polar map of rest and stress myocardial perfusion, with the darkest region representing the region of greatest perfusion deficit. The top panel represents a patient with ischemia (case) with PFV, TFV, and CCS of 136 cm³, 227 cm³, and 150, respectively; the bottom panel represents a control patient with PFV, TFV, and CCS of 90 cm³, 198 cm³, and 177, respectively. CCS = coronary calcium score; other abbreviations as in Figure 1.

Figure 3. Ischemia Prediction With Addition of PFV

Receiver-operator characteristic curves for prediction of ischemia using logarithmic transform of the CCS alone (red) and CCS plus logarithmic transform of PFV (green). AUC = area under the curve; other abbreviations as in Figures 1 and 2.

Figure 4. Ischemia Prediction With Addition of TFV

Receiver-operator characteristic curves for prediction of ischemia using logarithmic transform of CCS alone (red) and CCS plus logarithmic transform of TFV (green). Abbreviations as in Figures 1, 2, and 3.