Evaluation of Pericoronary Fat Attenuation Index to Better Identify Culprit Lesions in Acute Coronary Syndrome According to Stenosis Severity.

IF 1 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Lili Li, Jia Tang, Pinyan Fang, YuLin Sun, Yanan Gao, Hanxiong Qi, Bing Liu, Jiwang Zhang, Lijuan Fan
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引用次数: 0

Abstract

Objective: To investigate the incremental value of pericoronary fat attenuation index (FAI) in routine coronary artery computed tomography angiography (CCTA) to identify culprit lesions in acute coronary syndrome (ACS).

Methods: We reviewed the CCTA data from 80 ACS patients and 40 individuals with stable coronary atherosclerosis. ACS patient plaques were categorized into culprit and nonculprit groups. The plaque-specific pericoronary FAI was assessed using the Perivascular Fat Analysis Tool. We applied a default prespecified window of -190 to -30 Hounsfield units (HU) and a broader prespecified window of -190 to 20 HU. FAI values within these prespecified windows and the types and severity of plaque stenosis were compared across the 3 groups. Additionally, we investigated high-risk characteristics of plaques in the ACS group and their correlation with FAI. The effectiveness and worthiness of FAI in identifying culprit lesions were analyzed based on the receiver operating characteristic curve.

Results: The FAI values under the 2 prespecified windows were higher in the culprit group than in the nonculprit and control groups (all P < 0.001). The culprit group showed the most mixed plaques and the most severe stenosis (all P < 0.001). In the ACS group, the FAI value was significantly lower around calcified lesions (-85.00 ± 9.97 HU) than around noncalcified (-78.00 ± 11.52 HU) and mixed plaques (-78.00 ± 9.24 HU) (both P < 0.001). The culprit group had more high-risk plaques, and high-risk plaques had higher FAI values than those without high-risk characteristics (-70.00 ± 7.67 HU vs -82.00 ± 10.16 HU, P < 0.001). The efficacy of FAI under the default prespecified window in identifying culprit lesions was higher compared than that under the broader prespecified window (area under the curve = 0.799 vs 0.761, P = 0.042), and the diagnostic cutoff values were -77 versus -58 HU. The FAI under the default prespecified window exhibited an incremental value for identifying culprit lesions, as compared with stenosis severity (area under the curve = 0.970 vs 0.939, P < 0.001).

Conclusion: The culprit lesions have higher FAI than the nonculprit lesions and the controls. FAI is a worthy parameter for identifying culprit lesions in routine CCTA according to stenosis severity, and the default prespecified window is a better option.

评估冠状动脉周围脂肪衰减指数,根据狭窄严重程度更好地识别急性冠状动脉综合征的罪魁祸首病变
目的研究常规冠状动脉计算机断层扫描血管造影(CCTA)中冠状动脉周围脂肪衰减指数(FAI)在识别急性冠状动脉综合征(ACS)罪魁祸首病变方面的增量价值:我们回顾了 80 名 ACS 患者和 40 名稳定型冠状动脉粥样硬化患者的 CCTA 数据。我们将急性冠状动脉综合征患者的斑块分为罪魁祸首组和非罪魁祸首组。使用血管周围脂肪分析工具评估斑块特异性冠状动脉周围FAI。我们采用了一个默认的预设窗口,即 -190 到 -30 Hounsfield 单位 (HU),以及一个更宽的预设窗口,即 -190 到 20 HU。我们比较了 3 组患者在这些预设窗口内的 FAI 值以及斑块狭窄的类型和严重程度。此外,我们还研究了 ACS 组斑块的高风险特征及其与 FAI 的相关性。根据接收者操作特征曲线分析了 FAI 在识别罪魁祸首病变方面的有效性和价值:结果:罪魁祸首组在两个预设窗口下的 FAI 值高于非罪魁祸首组和对照组(所有 P <0.001)。罪魁祸首组显示出最多的混合斑块和最严重的狭窄(均P < 0.001)。在 ACS 组中,钙化病变周围的 FAI 值(-85.00 ± 9.97 HU)明显低于非钙化(-78.00 ± 11.52 HU)和混合斑块周围(-78.00 ± 9.24 HU)(均为 P <0.001)。罪魁祸首组有更多的高风险斑块,高风险斑块的 FAI 值高于无高风险特征的斑块(-70.00 ± 7.67 HU vs -82.00 ± 10.16 HU,P < 0.001)。默认预设窗口下的 FAI 在识别病灶方面的有效性高于更宽预设窗口下的 FAI(曲线下面积 = 0.799 vs 0.761,P = 0.042),诊断临界值为 -77 HU vs -58 HU。与狭窄严重程度相比,默认预设窗口下的 FAI 在识别罪魁祸首病变方面具有增量价值(曲线下面积 = 0.970 vs 0.939,P < 0.001):结论:罪魁祸首病变的 FAI 值高于非罪魁祸首病变和对照组。FAI是根据狭窄严重程度在常规CCTA中识别罪魁祸首病变的一个有价值的参数,而默认的预设窗口是一个更好的选择。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
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