Ultrasound-guided femoral access in patients with vascular closure devices: a prespecified analysis of the randomised UNIVERSAL trial.

BACKGROUND: Whether ultrasound (US)-guided femoral access compared to femoral access without US guidance decreases access site complications in patients receiving a vascular closure device (VCD) is unclear. AIMS: We aimed to compare the safety of VCD in patients undergoing US-guided versus non-US-guided femoral arterial access for coronary procedures. METHODS: We performed a prespecified subgroup analysis of the UNIVERSAL trial, a multicentre randomised controlled trial of 1:1 US-guided femoral access versus non-US-guided femoral access, stratified for planned VCD use, for coronary procedures on a background of fluoroscopic landmarking. The primary endpoint was a composite of major Bleeding Academic Research Consortium 2, 3 or 5 bleeding and vascular complications at 30 days. RESULTS: Of 621 patients, 328 (52.8%) received a VCD (86% ANGIO-SEAL, 14% ProGlide). In patients who received a VCD, those randomised to US-guided femoral access compared to non-US-guided femoral access experienced a reduction in major bleeding or vascular complications (20/170 [11.8%] vs 37/158 [23.4%], odds ratio [OR] 0.44, 95% confidence interval [CI]: 0.23-0.82). In patients who did not receive a VCD, there was no difference between the US- and non-US-guided femoral access groups, respectively (20/141 [14.2%] vs 13/152 [8.6%], OR 1.76, 95% CI: 0.80-4.03; interaction p=0.004). CONCLUSIONS: In patients receiving a VCD after coronary procedures, US-guided femoral access was associated with fewer bleeding and vascular complications compared to femoral access without US guidance. US guidance for femoral access may be particularly beneficial when VCD are used.

Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN.

PURPOSE:  The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. METHODS:  We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. RESULTS:  A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. CONCLUSION:  This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.