Objective The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. Results The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment. Data may be obtained from a third party and are not publicly available. Index data for the CREDENCE trial has been previously published. De-identified patient data are not publicly available, except if necessary to confirm study results; requests for data may be made by contacting Dr James Min (James.Min@cleerlyhealth.com).

中文翻译：

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使用人工智能的年龄、动脉粥样硬化和血管造影狭窄的关系

目的本研究利用人工智能定量冠状动脉计算机断层扫描血管造影（AI-QCT）评估冠状动脉狭窄、动脉粥样硬化斑块特征（APCs）和年龄的关系。方法 这是对 303 名参加 CREDENCE（心肌缺血的动脉粥样硬化决定因素的计算机断层扫描评估）试验的受试者的数据的事后分析，这些受试者被转诊进行侵入性冠状动脉造影，随后接受了冠状动脉 CT 血管造影 (CCTA)。在这项研究中，一个盲法核心实验室分析定量冠状动脉造影图像，将病变分类为阻塞性 (≥50%) 或非阻塞性 (<50%)，而 AI 软件量化 APC，包括斑块体积 (PV)、低密度非钙化斑块(LD-NCP)、非钙化斑块 (NCP)、钙化斑块 (CP)、基于 CCTA 成像的每个患者和每个病变的病变长度。斑块测量值针对血管体积进行标准化，并报告为所有相关斑块成分的百分比粥样斑块体积 (%PAV)。随后按年龄<65 岁和≥65 岁对数据进行分层。结果 该队列为 64.4±10.2 岁，女性占 29%。总体而言，>65 岁的患者比 <65 岁的患者有更多的 PV 和 CP。在病变水平上，在阻塞性病变（29.2 mm3 vs 48.2 mm3；p<0.04）和非阻塞性病变（22.1 mm3 vs 49.4 mm3；p<0.004）中，>65 岁患者的 CP 比年轻患者多，而年轻患者的 CP 比例更高PAV (LD-NCP) (1.5% vs 0.7%; p<0.038)。与非阻塞性病变相比，年轻患者阻塞性病变的 PV、LD-NCP、NCP 和病变长度更多。老年患者的病变类型之间没有观察到差异。结论 AI-QCT 识别出一个独特的 APC 特征，该特征因年龄和狭窄程度而异，并为 AI 引导的基于年龄的动脉粥样硬化识别、预防和治疗方法提供了基础。数据可能从第三方获得，并且不公开。CREDENCE 试验的索引数据之前已发布。去识别的患者数据不公开，除非有必要确认研究结果；可以联系 James Min 博士 (James.Min@cleerlyhealth.com) 提出数据请求。CREDENCE 试验的索引数据之前已发布。去识别的患者数据不公开，除非有必要确认研究结果；可以联系 James Min 博士 (James.Min@cleerlyhealth.com) 提出数据请求。CREDENCE 试验的索引数据之前已发布。去识别的患者数据不公开，除非有必要确认研究结果；可以联系 James Min 博士 (James.Min@cleerlyhealth.com) 提出数据请求。