Long-term prognostic value of CT-based high-risk coronary lesion attributes and radiomic features of pericoronary adipose tissue in diabetic patients

W Yang ¹, X Ding ², Y Yu ¹

⁰Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, China.

Clinical Radiology +

|

September 12, 2024

View abstract on PubMed

Summary

Coronary computed tomography angiography (CCTA) high-risk anatomical features effectively predict major adverse cardiac events (MACE) in diabetic patients. Pericoronary adipose tissue (PCAT) radiomic features did not add incremental prognostic value for MACE risk stratification.

Area Of Science

Cardiovascular Imaging
Radiology
Diabetic Complications

Background

Diabetic patients have a higher risk of major adverse cardiac events (MACE).
Coronary computed tomography angiography (CCTA) is used to assess coronary artery disease (CAD).
Pericoronary adipose tissue (PCAT) radiomic features are being explored for cardiovascular risk prediction.

Purpose Of The Study

To evaluate the prognostic value of CCTA-derived high-risk attributes and PCAT radiomic features for MACE in diabetic patients.
To compare the predictive performance of models incorporating clinical factors, CCTA imaging parameters, and PCAT radiomic features.

Main Methods

Prospective enrollment of diabetic patients with intermediate pre-test CAD probability undergoing CCTA.
Development and validation of three predictive models: clinical factors alone, clinical factors + CCTA parameters, and clinical factors + CCTA parameters + PCAT radiomic features.
Multivariable Cox regression analysis to identify independent predictors of MACE.

Main Results

HbA1c, coronary calcium Agatston score, significant stenosis, and high-risk plaque were independent MACE predictors.
CCTA imaging parameters significantly improved MACE prediction compared to clinical factors alone (C-index 0.79 vs. 0.68).
PCAT radiomic features did not provide incremental predictive value when added to clinical and CCTA parameters (C-index 0.79 vs. 0.80).

Conclusions

A combined model using clinical data and CCTA high-risk anatomical features demonstrates high efficacy for MACE prediction in diabetes.
PCAT radiomic features do not enhance the risk stratification of MACE in this diabetic patient cohort.
CCTA anatomical assessment remains crucial for cardiovascular risk assessment in diabetic patients.

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