Conectar
Outcomes associated with rapid response teams (RRTs) are inconsistent. This may be due to underlying facilitators and barriers to RRT activation that are affected by team leaders and health systems.
The aim of this study was to synthesize the published research about facilitators and barriers to nurse-led RRT activation in the United States (U.S.).
A systematic review was conducted. Four databases were searched from January 2000 to June 2023 for peer-reviewed quantitative, qualitative, and mixed methods studies reporting facilitators and barriers to RRT activation. Studies conducted outside the U.S. or with physician-led teams were excluded.
Twenty-five studies met criteria representing 240,140 participants that included clinicians and hospitalized adults. Three domains of facilitators and barriers to RRT activation were identified: (1) hospital infrastructure, (2) clinician culture, and (3) nurses' beliefs, attributes, and knowledge. Categories were identified within each domain. The categories of perceived benefits and positive beliefs about RRTs, knowing when to activate the RRT, and hospital-wide policies and practices most facilitated activation, whereas the categories of negative perceptions and concerns about RRTs and uncertainties surrounding RRT activation were the dominant barriers.
Facilitators and barriers to RRT activation were interrelated. Some facilitators like hospital leader and physician support of RRTs became barriers when absent. Intradisciplinary communication and collaboration between nurses can positively and negatively impact RRT activation. The expertise of RRT nurses should be further studied.
by John Efromson, Giuliano Ferrero, Aurélien Bègue, Thomas Jedidiah Jenks Doman, Clay Dugo, Andi Barker, Veton Saliu, Paul Reamey, Kanghyun Kim, Mark Harfouche, Jeffrey A. Yoder
Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.
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