Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4–5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4–9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9–63·9], p<0·001) or MACE (12·6 [8·5–18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17–8·82], p<0·001, for very high risk low or medium risk) and MACE (4·68 [3·93–5·57], p<0·001 for very high risk low or medium risk). Finally, the AI-Risk model was well calibrated against true events. The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

中文翻译：

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无阻塞性冠状动脉疾病患者的炎症风险和心血管事件：ORFAN 多中心纵向队列研究


冠状动脉计算机断层扫描血管造影（CCTA）是胸痛的一线检查，用于指导血运重建。然而，CCTA 的广泛采用表明，大量没有阻塞性冠状动脉疾病 (CAD) 的个体的预后和治疗尚不明确。使用血管周围脂肪衰减指数 (FAI) 评分通过 CCTA 测量冠状动脉炎症，可以实现心血管风险预测并指导无阻塞性 CAD 个体的管理。牛津风险因素和非侵入性成像 (ORFAN) 研究旨在评估接受 CCTA 的患者的风险状况和事件发生率，作为英国国家医疗服务体系 (NHS) 常规临床护理的一部分；检验冠状动脉炎症导致患有或不患有 CAD 患者的心脏死亡率或主要不良心脏事件 (MACE) 的假设；并从外部验证先前训练的人工智能 (AI) 风险预测算法和相关 AI 风险分类系统在英国人群中的性能。这项多中心、纵向队列研究包括在英国八家医院接受临床指征 CCTA 的 40091 名连续患者，并对他们进行 MACE（即心肌梗死、新发心力衰竭或心源性死亡）随访，中位随访时间为 2·7 年（ IQR 1·4–5·3)。在随访时间最长（7·7 年 [6·4–9·1]）的两家医院连续 3393 名患者中评估了 FAI 评分在是否存在阻塞性 CAD 方面的预后价值。然后在该人群中评估了人工智能增强的心脏风险预测算法，该算法集成了 FAI 评分、冠状动脉斑块指标和临床风险因素。 在中位随访 2·7 年期间，无阻塞性 CAD 患者（40 091 例中的 32 533 例 [81·1%]）占 4307 例 MACE 总数中的 2857 例 (66·3%) 和 1118 例 (63·7%) ）占整个队列 A 中 1754 例心脏死亡总数。所有三个冠状动脉的 FAI 评分增加对心脏死亡风险产生附加影响（风险比 [HR] 29·8 [95% CI 13·9– 63·9]，p<0·001) 或 MACE (12·6 [8·5–18·6]，p<0·001) 比较三个血管，每条动脉的 FAI 评分位于顶部与底部四分位数。任何冠状动脉的 FAI 评分均可预测心脏死亡率和 MACE，独立于心血管危险因素以及 CAD 的存在或程度。 AI-Risk 分类与心脏死亡率（6·75 [5·17–8·82]，p<0·001，极高风险、低风险或中等风险）和 MACE（4·68 [3·93 –5·57]，p<0·001（极高风险、低风险或中等风险）。最后，人工智能风险模型根据真实事件进行了很好的校准。 FAI 评分捕捉到的炎症风险超出了当前临床风险分层和 CCTA 解释的范围，特别是在没有阻塞性 CAD 的患者中。 AI-Risk 将这些信息集成到预测算法中，该算法可以用作传统基于风险因素的风险计算器的替代方案。英国心脏基金会、NHS-AI 奖、创新英国、国家健康与护理研究所和牛津生物医学研究中心。