Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model.

BACKGROUND: Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth. METHODS: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. RESULTS: Of 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, -6.82 to 35.1) and zone 5 (13.4%; IQR, -7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). CONCLUSIONS: We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

The biological role and future therapeutic uses of nitric oxide in extracorporeal membrane oxygenation, a narrative review.

BACKGROUND: Nitric oxide (NO) is a gas naturally produced by the human body that plays an important physiological role. Specifically, it binds guanylyl cyclase to induce smooth muscle relaxation. NO's other protective functions have been well documented, particularly its protective endothelial functions, effects on decreasing pulmonary vascular resistance, antiplatelet, and anticoagulation properties. The use of nitric oxide donors as vasodilators has been known since 1876. Inhaled nitric oxide has been used as a pulmonary vasodilator and to improve ventilation perfusion matching since the 1990s. It is currently approved by the United States Food and Drug Administration for neonates with hypoxic respiratory failure, however, it is used off-label for acute respiratory distress syndrome, acute bronchiolitis, and COVID-19. PURPOSE: In this article we review the currently understood biological action and therapeutic uses of NO through nitric oxide donors such as inhaled nitric oxide. We will then explore recent studies describing use of NO in cardiopulmonary bypass and extracorporeal membrane oxygenation and speculate on NO's future uses.

Reconsidering FDA Guidelines: A Single-Center Experience of Prolonged Impella 5.5 Support.

OBJECTIVE: Impella 5.5 (Abiomed, Danvers, MA, USA) is approved by the US Food and Drug Administration (FDA) for mechanical circulatory support for ≤14 days. It is unknown whether prolonged support is associated with worse outcomes. We sought to review our single-center experience with Impella 5.5 and compare outcomes based on support duration. METHODS: We retrospectively reviewed adult patients (≥18 years old) supported with Impella 5.5 at our institution (May 2020 to April 2023). Patients on prolonged support (>14 days) were compared with those supported for ≤14 days. RESULTS: There were 31 patients supported with Impella 5.5 including 14 (45.2%) supported >14 days. Median support duration for those on prolonged support was 43.5 (interquartile range [IQR] 25 to 63.5) days versus 8 (IQR 6, 13) days for those who were not (P < 0.001). Overall, the device-related complication rate was 9.7% and did not differ between groups (P = 0.08). Overall, 30-day postimplant survival was 71% and did not differ by support duration (P = 0.2). In-hospital mortality was 32% and did not differ between cohorts (P > 0.99). Among those surviving to explant (n = 22), long-term strategy included bridge to durable ventricular assist device (18%, n = 4), cardiac transplant (55%, n = 12), and cardiac recovery (27%, n = 6). CONCLUSIONS: High-risk patients with cardiogenic shock may be supported with Impella 5.5 beyond the FDA-approved duration without increased risk of complications or mortality.