Abstract：
BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization.
OBJECTIVE: 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).
OBJECTIVE: 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).
摘要：
背景：急性冠脉综合征（ACS）的病变级别风险预测需要更好的表征。
目的：本研究旨在探讨人工智能支持的定量冠状动脉斑块和血液动力学分析（AI-QCPHA）的附加价值。
方法：在ACS事件发生前1个月至3年接受冠状动脉CT血管造影（CTA）的ACS患者中，根据有创冠状动脉造影判定冠状动脉CTA上的罪犯和非罪犯病变。主要终点是ACS罪犯病变风险模型的可预测性。参考模型包括冠状动脉疾病报告和数据系统，狭窄严重程度的标准化分类，和高危斑块，定义为具有≥2个不良斑块特征的病变。新的预测模型是参考模型加上AI-QCPHA特征，通过派生队列中的分层聚类和信息增益选择。在验证队列中评估模型性能。
结果：在351名患者（年龄：65.9±11.7岁）中，有2,088名非罪犯和363名罪犯病变，从冠状动脉CTA到ACS事件的中位间隔为375天（Q1-Q3：95-645天），223例患者（63.5%）出现心肌梗死。在派生队列中(n=243)，最佳的AI-QCPHA特征是跨病变的血流储备分数，斑块负荷，总斑块体积,低衰减斑块体积，和平均总心肌血流量百分比。在验证队列中，添加AI-QCPHA特征显示出比参考模型更高的可预测性（n=108）（AUC：0.84vs0.78；P<0.001）。AI-QCPHA特征的相加值在冠状动脉CTA的不同时间点是一致的。
结论：与常规冠状动脉CTA分析相比，AI启用的斑块和血流动力学定量提高了ACS罪犯病变的可预测性。（使用冠状动脉计算机断层扫描血管造影和计算流体力学II[EMERALD-II]；NCT03591328探索急性冠状动脉综合征斑块破裂的机制）。
目的：本研究旨在探讨人工智能支持的定量冠状动脉斑块和血液动力学分析（AI-QCPHA）的附加价值。
方法：在ACS事件发生前1个月至3年接受冠状动脉CT血管造影（CTA）的ACS患者中，根据有创冠状动脉造影判定冠状动脉CTA上的罪犯和非罪犯病变。主要终点是ACS罪犯病变风险模型的可预测性。参考模型包括冠状动脉疾病报告和数据系统，狭窄严重程度的标准化分类，和高危斑块，定义为具有≥2个不良斑块特征的病变。新的预测模型是参考模型加上AI-QCPHA特征，通过派生队列中的分层聚类和信息增益选择。在验证队列中评估模型性能。
结果：在351名患者（年龄：65.9±11.7岁）中，有2,088名非罪犯和363名罪犯病变，从冠状动脉CTA到ACS事件的中位间隔为375天（Q1-Q3：95-645天），223例患者（63.5%）出现心肌梗死。在派生队列中(n=243)，最佳的AI-QCPHA特征是跨病变的血流储备分数，斑块负荷，总斑块体积,低衰减斑块体积，和平均总心肌血流量百分比。在验证队列中，添加AI-QCPHA特征显示出比参考模型更高的可预测性（n=108）（AUC：0.84vs0.78；P<0.001）。AI-QCPHA特征的相加值在冠状动脉CTA的不同时间点是一致的。
结论：与常规冠状动脉CTA分析相比，AI启用的斑块和血流动力学定量提高了ACS罪犯病变的可预测性。（使用冠状动脉计算机断层扫描血管造影和计算流体力学II[EMERALD-II]；NCT03591328探索急性冠状动脉综合征斑块破裂的机制）。
