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.
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.
Authors: Christine M Albert; Edwin G Nam; Eric B Rimm; Hong Wei Jin; Roger J Hajjar; David J Hunter; Calum A MacRae; Patrick T Ellinor Journal: Circulation Date: 2007-12-10 Impact factor: 29.690
Authors: Jingzhong Ding; Stephen B Kritchevsky; Tamara B Harris; Gregory L Burke; Robert C Detrano; Moyses Szklo; J Jeffrey Carr Journal: Obesity (Silver Spring) Date: 2008-05-29 Impact factor: 5.002
Authors: Guido A Rosito; Joseph M Massaro; Udo Hoffmann; Frederick L Ruberg; Amir A Mahabadi; Ramachandran S Vasan; Christopher J O'Donnell; Caroline S Fox Journal: Circulation Date: 2008-01-22 Impact factor: 29.690
Authors: Robert Detrano; Alan D Guerci; J Jeffrey Carr; Diane E Bild; Gregory Burke; Aaron R Folsom; Kiang Liu; Steven Shea; Moyses Szklo; David A Bluemke; Daniel H O'Leary; Russell Tracy; Karol Watson; Nathan D Wong; Richard A Kronmal Journal: N Engl J Med Date: 2008-03-27 Impact factor: 91.245
Authors: Karla M Pou; Joseph M Massaro; Udo Hoffmann; Ramachandran S Vasan; Pal Maurovich-Horvat; Martin G Larson; John F Keaney; James B Meigs; Izabella Lipinska; Sekar Kathiresan; Joanne M Murabito; Christopher J O'Donnell; Emelia J Benjamin; Caroline S Fox Journal: Circulation Date: 2007-08-20 Impact factor: 29.690
Authors: Sanjay Sarin; Christopher Wenger; Ajay Marwaha; Anwer Qureshi; Bernard D M Go; Cathleen A Woomert; Karla Clark; Louis A Nassef; Jamshid Shirani Journal: Am J Cardiol Date: 2008-07-02 Impact factor: 2.778
Authors: Damini Dey; Nathan D Wong; Balaji Tamarappoo; Ryo Nakazato; Heidi Gransar; Victor Y Cheng; Amit Ramesh; Ioannis Kakadiaris; Guido Germano; Piotr J Slomka; Daniel S Berman Journal: Atherosclerosis Date: 2009-08-21 Impact factor: 5.162
Authors: Ryo Nakazato; Haim Shmilovich; Balaji K Tamarappoo; Victor Y Cheng; Piotr J Slomka; Daniel S Berman; Damini Dey Journal: J Cardiovasc Comput Tomogr Date: 2011-03-21
Authors: Haim Shmilovich; Damini Dey; Victor Y Cheng; Ronak Rajani; Ryo Nakazato; Yuka Otaki; Rine Nakanishi; Piotr J Slomka; Louise E J Thomson; Sean W Hayes; John D Friedman; Heidi Gransar; Nathan D Wong; Leslee J Shaw; Matthew Budoff; Alan Rozanski; Daniel S Berman Journal: Am J Cardiol Date: 2011-08-30 Impact factor: 2.778
Authors: Andrew H Talman; Peter J Psaltis; James D Cameron; Ian T Meredith; Sujith K Seneviratne; Dennis T L Wong Journal: Cardiovasc Diagn Ther Date: 2014-12
Authors: Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick Journal: J Am Coll Cardiol Date: 2019-03-26 Impact factor: 24.094