本文由小咖机器人翻译整理

期刊来源：Lancet

原文链接：https://doi.org/10.1016/S0140-6736(24)00596-8

摘要内容如下：

背景

冠状动脉计算机断层扫描造影（CCTA）是胸痛的一线检查，用于指导血运重建。然而，CCTA的广泛应用揭示了一大群没有阻塞性冠状动脉疾病（CAD）的个体，其预后和治疗尚不明确。使用血管周围脂肪衰减指数（FAI）评分测量CCTA的冠状动脉炎症可以预测心血管风险，并指导非梗阻性CAD患者的治疗。牛津风险因素和非侵入性成像（ORFAN）研究旨在评估接受CCTA（作为英国国家医疗服务体系（NHS）常规临床护理的一部分）的患者的风险概况和事件发生率。在有或没有CAD的患者中，验证冠状动脉炎症驱动心脏死亡率或主要不良心脏事件（MACE）的假设；并在英国人群中外部验证先前训练的人工智能（AI）-风险预后算法和相关AI-风险分类系统的性能。

方法

这项多中心、纵向队列研究纳入了在英国8家医院接受临床指征CCTA的连续40091例患者，这些患者的MACE（即心肌梗死、新发心力衰竭或心源性死亡）随访时间中位数为2.7年（IQR 1.4-5.3）。在两家医院随访时间最长（7.7年[6.4-9.1]）的3393例连续患者中，评估了FAI评分在存在和不存在梗阻性CAD时的预后价值。然后在该人群中评估AI增强的心脏风险预测算法，该算法整合了FAI评分、冠状动脉斑块指标和临床风险因素。

调查结果

中位随访期为2.7年，在整个队列A中，非梗阻性CAD患者（32533例[81.1%]/40091例）占4307例总MACE的2857例（66.3%），占1754例总心源性死亡的1118例（63.7%）。所有三支冠状动脉的FAI评分增加对心源性死亡的风险有附加影响（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），比较三条血管，每条动脉的FAI评分在上四分位数和下四分位数。任何冠状动脉的FAI评分均可独立于心血管危险因素和CAD的存在或程度预测心源性死亡率和MACE。AI风险分级与心源性死亡率（6.75[5.17-8.82]，P<0.001，极高风险vs低或中等风险）和MACE（4.68[3.93-5.57]，P<0.001，极高风险vs低或中等风险）呈正相关。最后，根据真实事件对AI-Risk模型进行了很好的校准。

解释

FAI评分反映了超出当前临床风险分层和CCTA解释的炎症风险，特别是在没有梗阻性CAD的患者中。AI-RISK将这些信息整合到一个预测算法中，该算法可以作为传统的基于风险因素的风险计算器的替代方案。

英文原文如下：

Abstracts

BACKGROUND  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.

METHODS  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.

FINDINGS  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 vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events.

INTERPRETATION  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.

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