Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat

BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology and changes its composition in response to paracrine signals from the myocardium. We hypothesized that radiomic characterization of EAT from routine coronary computed tomographic angiography (CCTA) can noninvasively capture this adverse remodeling and enable early heart failure (HF) risk stratification. OBJECTIVES: We sought to develop and externally validate a reproducible radiomic signature of EAT associated with incident HF. METHODS: ). The model was developed in 59,327 individuals from 7 centers (age 57 ± 13 years, 47.5% female) and externally tested in 13,424 participants from 2 geographically distinct centers (58 ± 12 years, 49.4% female). Survival models were adjusted for age, sex, and conventional risk factors, including coronary artery disease (CAD) severity and EAT volume. RESULTS: to conventional risk models, including EAT volume and CAD severity, significantly improved 5-year discrimination (ΔAUC: 0.047; 95% CI: 0.029-0.065) and net reclassification (NRI: 0.39; 95% CI: 0.29-0.48) and suggested net clinical benefit on decision curve analysis. The associations were consistent across demographic subgroups and across the ejection fraction spectrum. CONCLUSIONS: Automated radiomic phenotyping of EAT from routine CCTA enables scalable, biologically informed stratification of future HF risk before clinical onset, positioning opportunistic imaging-based visceral fat profiling as a potential tool for precision prevention.

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Journal
Journal of the American College of Cardiology
Published
2026-04-01
DOI
https://doi.org/10.1016/j.jacc.2026.02.5116
Citations
1
Primary Topic
Cardiovascular Disease and Adiposity
Type
article
Field-Weighted Citation Impact
18.54

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article

Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat

C Shirodaria, G. Hudson, Alexios S. Antonopoulos, Keith M. Channon et al.
1 citations
Journal of the American College of Cardiology
Cardiovascular Disease and Adiposity
18.54
article

Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat

C Shirodaria, G. Hudson, Alexios S. Antonopoulos, Keith M. Channon, Nikant Sabharwal, Damaris Darby, Daniel Huck, Christos P. Kotanidis, Mohamed Marwan, Jon J. Denton, Lucy Kingham, Marly van Assen, Francesca Pugliese, Elizabeth Wahome, Saeed Mirsadraee, Ikboljon Sobirov, Attila Kardos, Chris Mathers, Andrew D Kelion, Rajesh Kharbanda, Rafail A. Kotronias, Steffen Erhard Petersen, Sheena Thomas, M. Siddique, Parijat Patel, ADRIAN P BANNING, Ahmad Abdullrahman, John Graby, Robyn Farrall, Yogesh Sohan, Wenhua Lin, Evangelos K. Oikonomou, Aparna Deshpande, Amrita Bajaj, Katerina Dangas, Intrajeet Das, P. J. Tomlins, Jonathan Rodrigues, N. Screaton, Susan Anthony, Sam Fry, D Adlam, Kenneth Chan, Tarun K. Mittal, Dan Lawday, Rohan Desai, Wendy Qin, Ronak Rajani, Weir-Macall, Mary Kasongo, Anne Rose, Colin Berry, Rohan Khera, David J. Murphy, Benjamin Hudson, Naohiko Takahashi, Jonathan, Bruno Silva Santos, Edward Nicol, Chrisha Ledesma, Guo-Wei He, John Deanfield, John P. Greenwood, Cheng Xie, Pál Maurovich-Horvat, Milind Y. Desai, Praveen Rao, Ron Blankstein, Stefan Neubauer, Charalambos Antoniades
article en
1 citations

Abstract

BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology and changes its composition in response to paracrine signals from the myocardium. We hypothesized that radiomic characterization of EAT from routine coronary computed tomographic angiography (CCTA) can noninvasively capture this adverse remodeling and enable early heart failure (HF) risk stratification. OBJECTIVES: We sought to develop and externally validate a reproducible radiomic signature of EAT associated with incident HF. METHODS: ). The model was developed in 59,327 individuals from 7 centers (age 57 ± 13 years, 47.5% female) and externally tested in 13,424 participants from 2 geographically distinct centers (58 ± 12 years, 49.4% female). Survival models were adjusted for age, sex, and conventional risk factors, including coronary artery disease (CAD) severity and EAT volume. RESULTS: to conventional risk models, including EAT volume and CAD severity, significantly improved 5-year discrimination (ΔAUC: 0.047; 95% CI: 0.029-0.065) and net reclassification (NRI: 0.39; 95% CI: 0.29-0.48) and suggested net clinical benefit on decision curve analysis. The associations were consistent across demographic subgroups and across the ejection fraction spectrum. CONCLUSIONS: Automated radiomic phenotyping of EAT from routine CCTA enables scalable, biologically informed stratification of future HF risk before clinical onset, positioning opportunistic imaging-based visceral fat profiling as a potential tool for precision prevention.

Journal of the American College of Cardiology
Brigham and Women's Hospital (US), Cleveland Clinic (US), St Thomas' Hospital (GB), Harvard University (US), Baker Heart and Diabetes Institute (AU), University of Leicester (GB), Queen Mary University of London (GB), Barts Health NHS Trust (GB), Leeds Teaching Hospitals NHS Trust (GB), Royal Papworth Hospital (GB), Emory University Hospital (US), Milton Keynes Hospital (GB), John Radcliffe Hospital (GB), Harefield Hospital (GB), Yale University (US), University of Oxford (GB), NIHR Royal Brompton Cardiovascular Biomedical Research Unit (GB), St. Thomas Hospital (CA), William Harvey Research Institute (GB), Royal United Hospital Bath NHS Trust (GB), NIHR Leicester Biomedical Research Centre (GB), University College London (GB)
British Heart Foundation, Innovate UK, NIHR Oxford Biomedical Research Centre, FP7 Coordination of Research Activities
Good health and well-being
Openalex Percentile: Top 1%
Cardiovascular Disease and Adiposity
18.54
DOIOpen Access PDFOpenAlex
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