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Title: Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.
Author: Koo BK, Yang S, Jung JW, Zhang J, Lee K, Hwang D, Lee KS, Doh JH, Nam CW, Kim TH, Shin ES, Chun EJ, Choi SY, Kim HK, Hong YJ, Park HJ, Kim SY, Husic M, Lambrechtsen J, Jensen JM, Nørgaard BL, Andreini D, Maurovich-Horvat P, Merkely B, Penicka M, de Bruyne B, Ihdayhid A, Ko B, Tzimas G, Leipsic J, Sanz J, Rabbat MG, Katchi F, Shah M, Tanaka N, Nakazato R, Asano T, Terashima M, Takashima H, Amano T, Sobue Y, Matsuo H, Otake H, Kubo T, Takahata M, Akasaka T, Kido T, Mochizuki T, Yokoi H, Okonogi T, Kawasaki T, Nakao K, Sakamoto T, Yonetsu T, Kakuta T, Yamauchi Y, Bax JJ, Shaw LJ, Stone PH, Narula J.
Journal: JACC Cardiovasc Imaging; 2024 Sep; 17(9):1062-1076. PubMed ID: 38752951.
Abstract:
BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

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