Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study.
Authors
Affiliations (20)
Affiliations (20)
- Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada.
- Deborah Heart and Lung Center, Browns Mills, NJ.
- Cardiology Division, Montefiore Health System/Albert Einstein College of Medicine, NY, USA.
- Department of Radiology (Nuclear Medicine), Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Nuclear Cardiology, Ignacio Chavez National Institute of Cardiology, Mexico City, Mexico.
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Ronald Reagan UCLA Medical Center, Los Angeles, California; West Los Angeles Veterans Affairs Medical Center, Los Angeles, California.
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Academic Institute, Houston, TX, USA.
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, USA.
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
- Intermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, UT, USA.
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
- Cardiovascular Imaging Program, Departments of Radiology and Medicine; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology; and Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Abstract
Epicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles. In this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator. Percentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95<sup>th</sup> sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50<sup>th</sup> percentile (p<0.001). Age- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.