Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France.
Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France.
Diagn Interv Imaging. 2023 Nov;104(11):547-551. doi: 10.1016/j.diii.2023.06.006. Epub 2023 Jun 17.
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.
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.
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%).
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.
本研究旨在评估通过使用深度学习重建和运动校正算法获得的 CT 图像,对经导管主动脉瓣置换术 CT(TAVI-CT)前的冠状动脉进行解读,从而安全避免进行冠状动脉造影的百分比。
筛选 2021 年 12 月至 2022 年 7 月期间行 TAVI-CT 和冠状动脉造影的所有连续患者,纳入本研究。排除有先前冠状动脉血运重建或未行 TAVI 的患者。所有 TAVI-CT 检查均采用深度学习重建和运动校正算法进行。在 TAVI-CT 检查中,回顾性分析冠状动脉的质量和狭窄程度。当图像质量不足和/或怀疑或诊断某一重要冠状动脉狭窄时,患者被认为有潜在的冠状动脉狭窄。冠状动脉造影的结果被用作显著 CAS 的参考标准。
共纳入 206 例患者(92 例男性;平均年龄 80.6 岁);其中 27/206(13%)例患者在冠状动脉造影上有显著的冠状动脉狭窄,被转介进行潜在的血运重建。TAVI-CT 识别需要冠状动脉血运重建的患者的敏感性、特异性、阴性预测值、阳性预测值和准确性分别为 100%(95%可信区间[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%)。质量和推荐冠状动脉造影的决策的观察者内和观察者间变异性为高度一致。平均阅读时间为 2±1.2(标准差)分钟(范围:1-5 分钟)。总体而言,TAVI-CT 可潜在地排除 97 例患者(47%)行血运重建的指征。
使用深度学习重建和运动校正算法对 TAVI-CT 上的冠状动脉进行分析,可能安全地避免 47%的患者进行冠状动脉造影。