RESUMEN
AIMS: Aortic valve calcification (AVC) of surgical valve bioprostheses (BP) has been poorly explored. We aimed to evaluate in-vivo and ex-vivo BP AVC and its prognosis value. METHODS AND RESULTS: Between 2011 and 2019, AVC was assessed using in-vivo computed tomography (CT) in 361 patients who had undergone surgical valve replacement 6.4±4.3 years earlier. Ex-vivo CT scans were performed for 37 explanted BP. The in-vivo CT scans were interpretable for 342 patients (19 patients [5.2%], were excluded). These patients were 77.2±9.1 years old and 64.3% were male. Mean in-vivo AVC was 307±500 Agatston unit (AU). The AVC was 562±570 AU for the 183 (53.5%) patients with structural valve degeneration (SVD) and 13±43 AU for those without SVD (p<0.0001). In-vivo and ex-vivo AVC were strongly correlated (r=0.88, p<0.0001). An in-vivo AVC>100 AU (n=147, 43%) had a specificity of 96% for diagnosing Stage 2-3 SVD (area under the curve=0.92). Patients with AVC>100 AU had a worse outcome compared with those with AVC≤100 AU (n=195). In multivariable analysis, AVC was a predictor of overall mortality (hazard ratio [HR] and 95% confidence interval=1.16[1.04-1.29]; p=0.006), cardiovascular mortality (HR=1.22[1.04-1.43]; p=0.013), cardiovascular events (HR=1.28 [1.16-1.41]; p<0.0001), and re-intervention (HR=1.15 [1.06-1.25]; p<0.0001). After adjustment for Stage 2-3 SVD diagnosis, AVC remained a predictor of overall mortality (HR=1.20 [1.04-1.39]; p=0.015) and cardiovascular events (HR=1.25 [1.09-1.43]; p=0.001). CONCLUSION: CT scan is a reliable tool to assess BP leaflet calcification. An AVC>100 AU is tightly associated with SVD and it is a strong predictor of overall mortality and cardiovascular events.
RESUMEN
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.
