Anatomical vs Physiological Lesion Characteristics in Prediction of Acute Coronary Syndrome.
Acute coronary syndrome (ACS) arises from a complex interplay among luminal narrowing, plaque morphology, and hemodynamic environment.
The authors aimed to compare the effectiveness of anatomy- and physiology-based ACS risk assessment.
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.
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.
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).
The authors aimed to compare the effectiveness of anatomy- and physiology-based ACS risk assessment.
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.
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.
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).
Authors
Yang Yang, Chung Chung, Park Park, Zhang Zhang, Lee Lee, Hwang Hwang, Lee Lee, Na Na, Doh Doh, Nam Nam, Kim Kim, Shin Shin, Chun Chun, Choi Choi, Kim Kim, Hong Hong, Park Park, Kim Kim, Husic Husic, Lambrechtsen Lambrechtsen, Jensen Jensen, Nørgaard Nørgaard, Andreini Andreini, Maurovich-Horvat Maurovich-Horvat, Merkely Merkely, Penicka Penicka, de Bruyne de Bruyne, Ihdayhid Ihdayhid, Ko Ko, Tzimas Tzimas, Leipsic Leipsic, Sanz Sanz, Rabbat Rabbat, Katchi Katchi, Shah Shah, Tanaka Tanaka, Nakazato Nakazato, Asano Asano, Terashima Terashima, Takashima Takashima, Amano Amano, Sobue Sobue, Matsuo Matsuo, Otake Otake, Kubo Kubo, Takahata Takahata, Akasaka Akasaka, Kido Kido, Mochizuki Mochizuki, Yokoi Yokoi, Okonogi Okonogi, Kawasaki Kawasaki, Nakao Nakao, Sakamoto Sakamoto, Yonetsu Yonetsu, Kakuta Kakuta, Yamauchi Yamauchi, Taylor Taylor, Bax Bax, Shaw Shaw, Stone Stone, Narula Narula, Koo Koo
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