Incremental Prognostic Value of Perivascular Fat Attenuation Index in Patients with Diabetes with Coronary Artery Disease

Purpose To investigate whether pericoronary adipose tissue attenuation (PCATa) provides incremental prognostic value over commonly used coronary CT angiography (CCTA) parameters for predicting major adverse cardiovascular and cerebrovascular events (MACCE) in individuals with diabetes mellitus (DM). Materials and Methods This prospective study included consecutive patients with type 2 DM who underwent CCTA due to suspected coronary artery disease between January 2015 and December 2017. PCATa of three coronary arteries was measured and evaluated. Cox proportional hazards regression was performed to investigate the prognostic value of PCATa for predicting MACCE. The incremental prognostic value of PCATa for MACCE was evaluated by comparing area under the receiver operating characteristic curve (AUC) values of four models (model 1: clinical characteristics, model 2: model 1 + conventional CCTA findings [coronary artery calcium score, Leiden score], model 3: model 2 + advanced CCTA findings [high-risk plaque, CT fractional flow reserve], model 4: model 3 + PCATa). Results Of the 1029 participants (mean age, 60.2 years ± 9.9 [SD]; 539 male) included in the study, 152 (14.8%) experienced MACCE during a median follow-up of 56.5 months. PCATa independently predicted MACCE after adjustment for clinical characteristics and commonly used CCTA findings (hazard ratio, 1.86 [95% CI: 1.24, 2.80]; <i>P</i> = .003). The model incorporating PCATa improved predictive performance for MACCE compared with the model including clinical characteristics and conventional and advanced CCTA parameters (AUC, 0.75 [95% CI: 0.71, 0.79] vs 0.73 [95% CI: 0.68, 0.77]; <i>P</i> = .009). Conclusion PCATa provided incremental prognostic value beyond clinical characteristics and other CCTA findings for prediction of MACCE in individuals with DM. <b>Keywords:</b> CT Angiography, Cardiac, Coronary Arteries, Inflammation, Outcomes Analysis, Coronary Computed Tomography Angiography, Diabetes Mellitus, Coronary Inflammation, Pericoronary Adipose Tissue Attenuation <i>Supplemental material is available for this article.</i> © RSNA, 2025.

Authors

Institutions

Publication Details

Journal
Radiology Cardiothoracic Imaging
Published
2025-03-05
DOI
https://doi.org/10.1148/ryct.240242
Citations
5
Primary Topic
Cardiovascular Disease and Adiposity
Type
article
Field-Weighted Citation Impact
6.94

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Incremental Prognostic Value of Perivascular Fat Attenuation Index in Patients with Diabetes with Coronary Artery Disease

Ran Xin, Dongkai Shan, Guanhua Dou, Yipu Ding et al.
5 citations
Radiology Cardiothoracic Imaging
Cardiovascular Disease and Adiposity
6.94
article

Incremental Prognostic Value of Perivascular Fat Attenuation Index in Patients with Diabetes with Coronary Artery Disease

Ran Xin, Dongkai Shan, Guanhua Dou, Yipu Ding, Guanxi Chen, Junjie Yang, Zinuan Liu, He Bai, Jing Jing, Yundai Chen, Tian-Wen Han, Xi Wang, Ziqiang Guo
article en
5 citations

Abstract

Purpose To investigate whether pericoronary adipose tissue attenuation (PCATa) provides incremental prognostic value over commonly used coronary CT angiography (CCTA) parameters for predicting major adverse cardiovascular and cerebrovascular events (MACCE) in individuals with diabetes mellitus (DM). Materials and Methods This prospective study included consecutive patients with type 2 DM who underwent CCTA due to suspected coronary artery disease between January 2015 and December 2017. PCATa of three coronary arteries was measured and evaluated. Cox proportional hazards regression was performed to investigate the prognostic value of PCATa for predicting MACCE. The incremental prognostic value of PCATa for MACCE was evaluated by comparing area under the receiver operating characteristic curve (AUC) values of four models (model 1: clinical characteristics, model 2: model 1 + conventional CCTA findings [coronary artery calcium score, Leiden score], model 3: model 2 + advanced CCTA findings [high-risk plaque, CT fractional flow reserve], model 4: model 3 + PCATa). Results Of the 1029 participants (mean age, 60.2 years ± 9.9 [SD]; 539 male) included in the study, 152 (14.8%) experienced MACCE during a median follow-up of 56.5 months. PCATa independently predicted MACCE after adjustment for clinical characteristics and commonly used CCTA findings (hazard ratio, 1.86 [95% CI: 1.24, 2.80]; <i>P</i> = .003). The model incorporating PCATa improved predictive performance for MACCE compared with the model including clinical characteristics and conventional and advanced CCTA parameters (AUC, 0.75 [95% CI: 0.71, 0.79] vs 0.73 [95% CI: 0.68, 0.77]; <i>P</i> = .009). Conclusion PCATa provided incremental prognostic value beyond clinical characteristics and other CCTA findings for prediction of MACCE in individuals with DM. <b>Keywords:</b> CT Angiography, Cardiac, Coronary Arteries, Inflammation, Outcomes Analysis, Coronary Computed Tomography Angiography, Diabetes Mellitus, Coronary Inflammation, Pericoronary Adipose Tissue Attenuation <i>Supplemental material is available for this article.</i> © RSNA, 2025.

Radiology Cardiothoracic ImagingVol. 7(2)
Nankai University (CN), Chinese PLA General Hospital (CN), PLA Academy of Military Science (CN)
National Natural Science Foundation of China, Beijing Nova Program, National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 3%
Cardiovascular Disease and Adiposity
6.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.