Introduction: Ultrasound (US) performed prior to percutaneous tracheostomy (PT) may be useful in avoiding injury to pretracheal vascular structures and in avoiding high placement of the tube. Bedside real-time US guidance with visualization of needle path is routinely utilized for other procedures such as central venous catheterization, and may enhance the safety and accuracy of PT without causing airway occlusion or hypercarbia. Our objective was to demonstrate that PT performed under real-time US guidance with visualization of needle path during tracheal puncture is feasible, including in patients with features that increase the technical difficulty of PT.
Methods: Mechanically ventilated patients with acute brain injury requiring tracheostomy underwent US guided PT. The orotracheal tube was withdrawn using direct laryngoscopy. The trachea was punctured under real-time US guidance (with visualization of the needle path) while using the acoustic shadows of the cricoid and the tracheal rings to identify the level of puncture. After guidewire passage the site and level of entry was verified using the bronchoscope, which was then withdrawn. Following dilatation and tube placement, placement in the airway was confirmed using auscultation and the "lung sliding" sign on US. Bronchoscopy and chest X-ray were then performed to identify any complications.
Results: Thirteen patients successfully underwent US guided PT. Three patients were morbidly obese, two were in cervical spine precautions and one had a previous tracheostomy. In all 13 patients bronchoscopy confirmed that guidewire entry was through the anterior wall and between the first and fifth tracheal rings. There was no case of tube misplacement, pneumothorax, posterior wall injury, significant bleeding or other complication during the procedure.
Conclusions: Percutaneous tracheostomy performed under real-time ultrasound guidance is feasible and appears accurate and safe, including in patients with morbid obesity and cervical spine precautions. Larger studies are required to further define the safety and relative benefits of this technique.
Trial Registration: UMIN Clinical Trials Registry, UMIN000005023.
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http://dx.doi.org/10.1186/cc10047 | DOI Listing |
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College of Life Science and Technology, Harbin Normal University, Harbin 150001, PR China. Electronic address:
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January 2025
Department of Biomedical Engineering, University of Cincinnati, UC Bioscience Center, 3159 Eden Ave., Cincinnati, Ohio, 45221, UNITED STATES.
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January 2025
Echosens, Paris, France.
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January 2025
College of Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania, USA.
Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI.
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