SUGAMMADEX versus neostigmine after ROCURONIUM continuous infusion in patients undergoing liver transplantation

Background: Rapid neuromuscular block reversal at the end of major abdominal surgery is recommended to avoid any postoperative residual block. To date, no study has evaluated sugammadex performance after rocuronium administration in patients undergoing liver transplantation. This is a randomized controlled trial with the primary objective of assessing the neuromuscular transmission recovery time obtained with sugammadex versus neostigmine after rocuronium induced neuromuscular blockade in patients undergoing orthotopic liver transplantation. Methods: The TOF-Watch SX\uae, calibrated and linked to a portable computer equipped with TOF-Watch SX Monitor Software\uae, was used to monitor and record intraoperative neuromuscular block maintained with a continuous infusion of rocuronium. Anaesthetic management was standardized as per our institution's internal protocol. At the end of surgery, neuromuscular moderate block reversal was obtained by administration of 2 mg/kg of sugammadex or 50 mcg/kg of neostigmine (plus 10 mcg/kg of atropine). Results: Data from 41 patients undergoing liver transplantation were analysed. In this population, recovery from neuromuscular block was faster following sugammadex administration than neostigmine administration, with mean times\ub1SD of 9.4 \ub1 4.6 min and 34.6 \ub1 24.9 min, respectively (p < 0.0001). Conclusion: Sugammadex is able to reverse neuromuscular block maintained by rocuronium continuous infusion in patients undergoing liver transplantation. The mean reversal time obtained with sugammadex was significantly faster than that for neostigmine. It is important to note that the sugammadex recovery time in this population was found to be considerably longer than in other surgical settings, and should be considered in clinical practice. Trial registration: ClinicalTrials.gov NCT02697929 (registered 3rd March 2016)

Thoracic ultrasound for pleural effusion in the intensive care unit: A narrative review from diagnosis to treatment

Pleural effusion (PLEFF), mostly caused by volume overload, congestive heart failure, and pleuropulmonary infection, is a common condition in critical care patients. Thoracic ultrasound (TUS) helps clinicians not only to visualize pleural effusion, but also to distinguish between the different types. Furthermore, TUS is essential during thoracentesis and chest tube drainage as it increases safety and decreases life-threatening complications. It is crucial not only during needle or tube drainage insertion, but also to monitor the volume of the drained PLEFF. Moreover, TUS can help diagnose co-existing lung diseases, often with a higher specificity and sensitivity than chest radiography and without the need for X-ray exposure. We review data regarding the diagnosis and management of pleural effusion, paying particular attention to the impact of ultrasound. Technical data concerning thoracentesis and chest tube drainage are also provided

Phantom model and scoring system to assess ability in ultrasound-guided chest drain positioning

Background: Chest tube positioning is an invasive procedure associated with potentially serious injuries. In the last few years, we have been running a project directed at developing a practical simulator of a surgical procedure taught on our medical training program. The phantom model reconstructs the pleural anatomy, visible by lung ultrasound, used for the assessed performance of the Seldinger technique. The aim of the present study was to investigate the validity of this simulation technology for assessing residents in anesthesia and intensive care medicine; specifically, their skill in positioning a US-guided chest tube drain was tested using the simulator device. The second aim of the paper was to evaluate the learning curve of our residents over their 5-year study course and validate the phantom scoring system. Methods: This was a prospective, single-blinded observational study. Participants were recruited from residents in anesthesia and intensive care medicine and divided into two groups: \u2018Novice\u2019 and \u2018Expert,\u2019 based on the course year attended (years 1, 2, and 3 vs. years 4 and 5, respectively). We asked them to position a chest tube drain in a phantom model, guided by ultrasound, to drain a simulated pleural effusion. Each subject performed two tests that simulated pleural effusions of 4 and 2\ua0cm, respectively. Every step of the maneuver was constantly monitored and the performance scored by the investigators. We then performed a Spearman correlation analysis to evaluate the effect of experience level on the performance of the two groups of residents. Results: Thirty-one residents were included in this study: 20 in the Novice group and 11 in the Expert group. The mean performance rating score was 0.75\ua0\ub1\ua04.38 for the Novice Group and 5.91\ua0\ub1\ua03.75 for the Expert group (p\ua0=\ua00.0026). The Spearman correlation analysis examining the relationship between year of residency and performance rating score confirmed a positive correlation (r\ua0=\ua00.58, p\ua0=\ua00.0006). Post-test trend analysis revealed a statistically significant linear trend for skill growth across time, i.e., course year (p\ua0=\ua00.0022). Conclusions: Our simulated procedure using a phantom model of lung anatomy can accurately and reliably be used to assess the skill levels of operators in their ability to drain pleural effusion

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC