经导管主动脉瓣置入术前的冠状动脉评估可以避免额外的冠状动脉造影

IF 4.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Adrien Lecomte , Aude Serrand , Lara Marteau , Baptiste Carlier , Thibaut Manigold , Vincent Letocart , Karine Warin Fresse , Jean-Michel Nguyen , Jean-Michel Serfaty
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引用次数: 1

摘要

本研究的目的是利用深度学习重建和运动校正算法获得的CT图像,通过对经导管前主动脉瓣植入CT (TAVI-CT)上的冠状动脉进行解释,评估冠状动脉造影可以安全避免的百分比。材料和方法从2021年12月至2022年7月,所有连续接受TAVI-CT和冠状动脉造影的患者被筛选纳入研究。既往有冠状动脉重建术或未接受TAVI的患者被排除在外。所有TAVI-CT检查结果均采用深度学习重建和运动校正算法。回顾性分析TAVI-CT检查、冠状动脉质量及狭窄情况。当图像质量不足和/或当诊断或怀疑有一个明显的冠状动脉狭窄时,认为患者可能有冠状动脉狭窄。冠状动脉造影结果作为显著CAS的参考标准。结果共206例患者(男性92例;平均年龄80.6岁);其中27/206(13%)在冠状动脉造影中有明显的冠状动脉狭窄,并被转诊进行潜在的血运重建。TAVI-CT识别需要冠状动脉重建术患者的敏感性、特异性、阴性预测值、阳性预测值和准确性分别为100%(95%置信区间[CI]: 87.2-100%)、100% (95% CI: 96.3-100%)、54% (95% CI: 46.6-61.6)、25% (95% CI: 17.0-34.0%)和60% (95% CI: 53.1-66.9%)。观察者内部和观察者之间的差异在质量和推荐冠状动脉造影术的决定上是一致的。平均读数时间为2±1.2(标准差)min(范围:1-5 min)。总体而言,TAVI-CT可以潜在地排除97例患者(47%)的血运重建指征。结论采用深度学习重建和运动校正算法在TAVI-CT上对冠状动脉进行分析,47%的患者可以安全地避免冠状动脉造影。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography

Purpose

The purpose of this study was to evaluate the percentage of coronary angiography that can be securely avoided by the interpretation of coronary arteries on pre transcatheter aortic valve implantation CT (TAVI-CT), using CT images obtained with deep-learning reconstruction and motion correction algorithms.

Material and method

All consecutive patients who underwent TAVI-CT and coronary angiography, from December 2021 to July 2022 were screened for inclusion in the study. Patients who had previous coronary artery revascularization or who did not undergo TAVI were excluded. All TAVI-CT examinations were obtained using deep-learning reconstruction and motion correction algorithms. On TAVI-CT examinations, quality and stenosis of coronary artery were analyzed retrospectively. When insufficient image quality and/or when diagnosis or doubt of one significant coronary artery stenosis, patients were considered as having possible coronary artery stenosis. The results of coronary angiography were used as the standard of reference for significant CAS.

Results

A total of 206 patients (92 men; mean age, 80.6 years) were included; of these 27/206 (13%) had significant coronary artery stenosis on coronary angiography and were referred for potential revascularization. Sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of TAVI-CT to identify patients requiring coronary artery revascularization was 100% (95% confidence interval [CI]: 87.2–100%), 100% (95% CI: 96.3–100%), 54% (95% CI: 46.6–61.6), 25% (95% CI: 17.0–34.0%) and 60% (95% CI: 53.1–66.9%) respectively. Intra- and inter observer variability was substantial agreement for quality and decision to recommend coronary angiography. Mean reading time was 2 ± 1.2 (standard deviation) min (range: 1–5 min). Overall, TAVI-CT could potentially rule out indication for revascularization for 97 patients (47%).

Conclusion

Analysis of coronary artery on TAVI-CT using deep-learning reconstruction and motion correction algorithms can potentially safely avoid coronary angiography in 47% of patients.

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来源期刊
Diagnostic and Interventional Imaging
Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
8.50
自引率
29.10%
发文量
126
审稿时长
11 days
期刊介绍: Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.
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