Relationship of epicardial fat volume from noncontrast CT with impaired myocardial flow reserve by positron emission tomography

Journal of Cardiovascular Computed Tomography

Volumn 9, Issue 4, 2015, Pages 303-309

  Otaki, Yuka ^a   Hell, Michaela ^b   Slomka, Piotr J ^a   Schuhbaeck, Annika ^b   Gransar, Heidi ^a   Huber, Brandi ^a   Nakazato, Ryo ^a   Germano, Guido ^a   Hayes, Sean W ^a   Thomson, Louise E J ^a   Friedman, John D ^a   Achenbach, Stephan ^b   Berman, Daniel S ^a   Dey, Damini ^a  

^a CEDARS SINAI MEDICAL CENTER   (United States)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Coronary calcium scoring; Coronary vascular dysfunction; Epicardial fat volume; Myocardial flow reserve; Noncontrast CT

Indexed keywords

FAT; RUBIDIUM 82;

AGED; ARTICLE; CARDIAC IMAGING; CARDIOVASCULAR PARAMETERS; CARDIOVASCULAR RISK; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; CORONARY ARTERY BLOOD FLOW; CORONARY FLOW RESERVE; EPICARDIAL FAT VOLUME; EPICARDIUM; FEMALE; HUMAN; LEARNING ALGORITHM; MALE; PET-CT SCANNER; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; RETROSPECTIVE STUDY; RISK ASSESSMENT; VERY ELDERLY; ADIPOSE TISSUE; COMPUTER ASSISTED DIAGNOSIS; CORONARY ANGIOGRAPHY; CORONARY ARTERY DISEASE; FRACTIONAL FLOW RESERVE; OBESITY; ORGAN SIZE; PATHOPHYSIOLOGY; PERICARDIUM; PROCEDURES; PROGNOSIS; RADIOGRAPHY; REPRODUCIBILITY; SCINTISCANNING; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ADIPOSE TISSUE; ADIPOSITY; AGED; CORONARY ANGIOGRAPHY; CORONARY ARTERY DISEASE; FEMALE; FRACTIONAL FLOW RESERVE, MYOCARDIAL; HUMANS; IMAGING, THREE-DIMENSIONAL; MALE; ORGAN SIZE; PERICARDIUM; PROGNOSIS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84930926967     PISSN: 19345925     EISSN: 1876861X     Source Type: Journal    
DOI: 10.1016/j.jcct.2015.03.005     Document Type: Article

References (33)

1. Iacobellis G., Corradi D., Sharma A.M. Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart. Nat Clin Pract Cardiovasc Med 2005, 2:536-543.
2. Mazurek T., Zhang L., Zalewski A., et al. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation 2003, 108:2460-2466.
3. Rosito G.A., Massaro J.M., Hoffmann U., et al. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham heart study. Circulation 2008, 117:605-613.
4. Dey D., Wong N.D., Tamarappoo B.K., et al. Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome. Atherosclerosis 2010, 209:136-141.
5. Ding J., Kritchevsky S.B., Harris T.B., et al. The association of pericardial fat with calcified coronary plaque. Obesity 2008, 16:1914-1919.
6. Yerramasu A., Dey D., Venuraju S., et al. Increased volume of epicardial fat is an independent risk factor for accelerated progression of sub-clinical coronary atherosclerosis. Atherosclerosis 2011, 220:223-230.
7. Nakanishi R., Rajani R., Cheng V.Y., et al. 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. Atherosclerosis 2011, 218:363-368.
8. Tamarappoo B., Dey D., Shmilovich H., et al. Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT. JACC Cardiovasc Imaging 2010, 3:1104-1112.
9. Janik M., Hartlage G., Alexopoulos N., et al. Epicardial adipose tissue volume and coronary artery calcium to predict myocardial ischemia on positron emission tomography-computed tomography studies. J Nucl Cardiol 2010, 17:841-847.
10. Cheng V.Y., Dey D., Tamarappoo B.K., et al. Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events on 4-year follow-up: a case-control study. JACC Cardiovasc Imaging 2010, 3:352-360.
11. Mahabadi A.A., Massaro J.M., Rosito G.A., et al. Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the Framingham heart study. Eur Heart J 2009, 30(7):850-856.
12. Ding J., Hsu F.C., Harris T.B., et al. The association of pericardial fat with incident coronary heart disease: the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2009, 90:499-504.
13. Mahabadi A.A., Berg M.H., Lehmann N., et al. Association of epicardial fat with cardiovascular risk factors and incident myocardial infarction in the general population: the Heinz Nixdorf Recall Study. J Am Coll Cardiol 2013, 61:1388-1395.
14. Herzog B.A., Husmann L., Valenta I., et al. Long-term prognostic value of 13N-ammonia myocardial perfusion positron emission tomography added value of coronary flow reserve. J Am Coll Cardiol 2009, 54:150-156.
15. Murthy V.L., Naya M., Foster C.R., et al. Improved cardiac risk assessment with noninvasive measures of coronary flow reserve. Circulation 2011, 124:2215-2224.
16. Ziadi M.C., deKemp R.A., Williams K.A., et al. Impaired myocardial flow reserve on rubidium-82 positron emission tomography imaging predicts adverse outcomes in patients assessed for myocardial ischemia. J Am Coll Cardiol 2011, 58:740-748.
17. Townsend D., Jakoby B., Long M., et al. Performance and clinical workflow of a new combined PET/CT scanner. J Nucl Med 2007, 48:437.
18. Nakazato R., Berman D., Dey D., et al. Automated quantitative Rb-82 3D PET/CT myocardial perfusion imaging: normal limits and correlation with invasive coronary angiography. J Nucl Cardiol 2012, 19:265-276.
19. Agatston A.S., Janowitz W.R., Hildner F.J., Zusmer N.R., Viamonte M., Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990, 15:827-832.
20. Dekemp R.A., Declerck J., Klein R., et al. Multisoftware reproducibility study of stress and rest myocardial blood flow assessed with 3D dynamic PET/CT and a 1-tissue-compartment model of 82Rb kinetics. J Nucl Med 2013, 54:571-577.
21. Slomka P.J., Alexanderson E., Jacome R., et al. Comparison of clinical tools for measurements of regional stress and rest myocardial blood flow assessed with 13N-ammonia PET/CT. J Nucl Med 2012, 53:171-181.
22. Nakazato R., Shmilovich H., Tamarappoo B.K., et al. Interscan reproducibility of computer-aided epicardial and thoracic fat measurement from noncontrast cardiac CT. J Cardiovasc Comput Tomogr 2011, 5:172-179.
23. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat 2000, 28:337-407.
24. Freund Y., Schapire R.E. A decision-theoretic generalization of online learning and an application to boosting. J Computer Syst Sci 1997, 55:119-139.
25. Freund Y, Schapire RE. Experiments With a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conference. 1998:148-156.
26. Dettling M., Buhlmann P. Boosting for tumor classification with gene expression data. Bioinformatics 2003, 19:1061-1069.
27. Arsanjani R., Xu Y., Dey D., et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol 2013, 20:553-562.
28. Witten I.H., Frank E., Hall M.A. Data Mining: Practical Machine Learning Tools and Techniques 2011, Morgan Kaufmann Publishers, Burlington, MA. 3rd ed.
29. Molinaro A.M., Simon R., Pfeiffer R.M. Prediction error estimation: a comparison of resampling methods. Bioinformatics 2005, 21:3301-3307.
30. Pencina M.J., D'Agostino R.B., Pencina K.M., Janssens A.C.J.W., Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 2012, 176:473-481.
31. DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 2007, 44:837-845.
32. Alam M.S., Green R., de Kemp R., Beanlands R.S., Chow B.J. Epicardial adipose tissue thickness as a predictor of impaired microvascular function in patients with non-obstructive coronary artery disease. J Nucl Cardiol 2013, 20:804-812.
33. Shmilovich H., Dey D., Cheng V.Y., et al. Threshold for the upper normal limit of indexed epicardial fat volume: derivation in a healthy population and validation in an outcome-based study. Am J Cardiol 2011, 108:1680-1685.