Anatomical vs Physiological Lesion Characteristics in Prediction of Acute Coronary Syndrome
Seokhun Yang1, Jae Wook Chung1, Sang-Hyeon Park1
1Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul National University of College of Medicine, Seoul, South Korea.
Insights
Assessing coronary artery plaque characteristics and blood flow dynamics using coronary computed tomography angiography (CCTA) helps predict acute coronary syndrome (ACS) events. Combining anatomical and physiological data improves risk prediction for future ACS.
Area of Science:
- Cardiology
- Radiology
- Biomedical Engineering
Background:
- Acute coronary syndrome (ACS) results from complex interactions between narrowed arteries, plaque characteristics, and blood flow.
- Accurate risk assessment is crucial for managing ACS patients.
Purpose of the Study:
- To compare the effectiveness of anatomy-based versus physiology-based risk assessment for ACS.
- To evaluate the diagnostic performance of coronary computed tomography angiography (CCTA) in identifying culprit lesions.
Main Methods:
- An international, multicenter study analyzed 351 ACS patients using CCTA data obtained 1 month to 3 years prior to the event.
- Lesions were classified as culprit or nonculprit. CCTA analysis included stenosis severity, adverse plaque characteristics (APC), plaque burden, and CCTA-derived fractional flow reserve (ΔFFRCT).
Main Results:
- All anatomical and physiological factors independently predicted culprit lesions (P < 0.001).
- Plaque burden ≥70% showed high sensitivity (90.6%) for identifying culprit lesions.
- ΔFFRCT ≥0.10 demonstrated high specificity (88.3%). Predictability was similar between ΔFFRCT and combined anatomical measures (AUC 0.805 vs 0.802).
Conclusions:
- Luminal narrowing, plaque characteristics, and hemodynamics are independent ACS predictors.
- Anatomical assessment offers sensitivity, while physiological assessment provides specificity.
- A comprehensive approach combining anatomical and physiological data refines ACS risk prediction.
Background:
Acute coronary syndrome (ACS) arises from a complex interplay among luminal narrowing, plaque morphology, and hemodynamic environment.
Objectives:
The authors aimed to compare the effectiveness of anatomy- and physiology-based ACS risk assessment.
Methods:
In this international, multicenter, internal case-control study, 351 ACS patients who underwent coronary computed tomography angiography (CCTA) 1 month to 3 years before the event were analyzed. Lesions were classified as culprit or nonculprit based on invasive coronary angiography at the time of ACS. Core lab CCTA analyses assessed lesion-specific characteristics: stenosis severity, adverse plaque characteristics (APC) (low-attenuation plaque, positive remodeling, spotty calcification, napkin-ring sign), plaque burden at minimum lumen area, and changes in CCTA-derived fractional flow reserve (ΔFFRCT). Diagnostic performance in identifying culprit lesions was compared.
Results:
Among 2,451 lesions, 363 (14.8%) became ACS culprits, with a median interval of 375 [95.0-644.5] days. All anatomical and simulated physiological characteristics were independently associated with culprit lesions (all P < 0.001). In identifying ACS culprit lesions, plaque burden ≥70% showed the highest sensitivity of 90.6% (87.2%-93.2%) and ΔFFRCT ≥0.10 had the highest specificity of 88.3% (86.9%-89.6%) %. Predictability was similar between ΔFFRCT and the combined degree of stenosis, the number of APCs, and plaque burden (area under the curve 0.805 [0.782-0.829] vs 0.802 [0.777-0.826]; P = 0.748), with additive discrimination towards each other.
Conclusions:
Luminal narrowing, plaque quality and quantity, and local hemodynamics were independent predictors of ACS, offering specificity in physiology and sensitivity in anatomy. A comprehensive assessment of them further refined the risk prediction for future ACS. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamics II [EMERALD II]; NCT03591328).
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