FFRCT of 0.8 or less is a predictor of long-term clinical outcomes and is superior to clinically significant stenosis on coronary CT angiograms. A normal FFRCT is associated with favorable long-ter... BackgroundCoronary CT angiography with noninvasive fractional flow reserve (FFR) predicts lesion-specific ischemia when compared with invasive FFR. The longer term prognostic value of CT-derived FF...

Leaflet thrombosis (LT) has become increasingly recognised following transcatheter and surgical aortic bioprosthetic valve (ABV) replacement and can be reliably identified by multidetector computed tomography (MDCT). However, it is an ongoing debate whether MDCT-defined LT is associated with adverse cerebrovascular outcomes. We sought to perform a systematic review and meta-analysis in order to assess the incidence and clinical outcomes associated with MDCT-defined leaflet thrombosis following (ABV) replacement. Electronic databases were searched for studies that performed mandatory MDCT imaging following ABV replacement. The primary endpoint was the incidence of cerebrovascular events, defined as a composite of stroke or transient ischaemic attack (TIA). Secondary endpoints included major adverse cerebrovascular and cardiovascular events (MACCE), stroke, TIA, death or myocardial infarction. In total, six studies met the inclusion criteria with 11.6% (198/1,704) of patients having MDCT-defined LT. The prevalence of LT following transcatheter and surgical ABV replacement was 13.2% and 3.6%, respectively. Cerebrovascular events were significantly increased in patients with LT (odds ratio [OR] 3.38, 95% CI: 1.78-6.41, p<0.001). The risk of MACCE (OR 2.10, 95% CI: 1.21-3.64, p<0.001) and TIA (OR 5.86, 95% CI: 2.05-16.75, p<0.001) was also increased in patients with LT, although there were no differences in the incidence of stroke (OR 2.43, 95% CI: 1.00-5.93, p=0.05), death (OR 0.92, 95% CI: 0.42-2.03, p=0.84) or myocardial infarction (OR 1.72, 95% CI: 0.34-9.78, p=0.54) between groups. MDCT-defined LT following ABV replacement is associated with a significantly increased risk of adverse cerebrovascular events. Further prospective studies are required to ascertain whether LT can be prevented or treated with pharmacological strategies.

Background: Coronary artery disease is common in patients with severe aortic stenosis. Computed tomography-derived fractional flow reserve (CT-FFR) is a clinically used modality for assessing coronary artery disease, however, its use has not been validated in patients with severe aortic stenosis. This study assesses the safety, feasibility, and validity of CT-FFR in patients with severe aortic stenosis. Methods: Prospectively recruited patients underwent standard-protocol invasive FFR and coronary CT angiography (CTA). CTA images were analyzed by central core laboratory (HeartFlow, Inc) for independent evaluation of CT-FFR. CT-FFR data were compared with FFR (ischemia defined as FFR ≤0.80). Results: Forty-two patients (68 vessels) underwent FFR and CTA; 39 patients (92.3%) and 60 vessels (88.2%) had interpretable CTA enabling CT-FFR computation. Mean age was 76.2±6.7 years (71.8% male). No patients incurred complications relating to premedication, CTA, or FFR protocol. Mean FFR and CT-FFR were 0.83±0.10 and 0.77±0.14, respectively. CT calcium score was 1373.3±1392.9 Agatston units. On per vessel analysis, there was positive correlation between FFR and CT-FFR (Pearson correlation coefficient, R =0.64, P <0.0001). Sensitivity, specificity, positive predictive value, and negative predictive values were 73.9%, 78.4%, 68.0%, and 82.9%, respectively, with 76.7% diagnostic accuracy. The area under the receiver-operating characteristic curve for CT-FFR was 0.83 (0.72–0.93, P <0.0001), which was higher than that of CTA and quantitative coronary angiography ( P =0.01 and P <0.001, respectively). Bland-Altman plot showed mean bias between FFR and CT-FFR as 0.059±0.110. On per patient analysis, the sensitivity, specificity, positive predictive, and negative predictive values were 76.5%, 77.3%, 72.2%, and 81.0% with 76.9% diagnostic accuracy. The per patient area under the receiver-operating characteristic curve analysis was 0.81 (0.67–0.95, P <0.0001). Conclusions: CT-FFR is safe and feasible in patients with severe aortic stenosis. Our data suggests that the diagnostic accuracy of CT-FFR in this cohort potentially enables its use in clinical practice and provides the foundation for future research into the use of CT-FFR for coronary evaluation pre-aortic valve replacement.

Objectives: Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. Methods: Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1–10, 11–100, 101–400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). Results: Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman’s r = 0.90, 95% confidence interval [CI] 0.89–0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98–0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland–Altman analysis demonstrated little difference (1.69, 95% limits of agreement: −41.22, 44.60) and there was almost excellent agreement (Cohen’s κ = 0.90, 95% CI 0.88–0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. Conclusions: This artificial intelligence–based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. Key Points: • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)–based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

The Official Journal of EuroPCR and the European Association of Percutaneous Cardiovascular Interventions (EAPCI)