DUGi

Impact of personalized tracheostomy tubes using 3d models in polytrauma patients: a single center, randomized, prospective, controlled clinical trial

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Background: Polytrauma is a leading cause of mortality and morbidity worldwide, frequently requiring tracheostomies for prolonged airway management, particularly those with severe injuries necessitating mechanical ventilation. Standard tracheostomy tubes (TT) often fail to adapt to individual anatomy, leading to complications such as tracheal stenosis, tracheomalacia or airway obstruction. Recent advancements in medical imaging and 3D printing technologies offer the possibility of customizing TTs to fit the unique anatomy of each patient, potentially reducing complications and improving outcomes. Utilizing high-resolution CT scans, 3D-printed TTs tailored to each patient’s specific anatomy could represent a paradigm shift in airway management for this highrisk population. Objectives: This study aims to evaluate whether customized TTs, designed from patient-specific 3D models based on routine CT scans, reduce complications compared to standard TTs. Secondary objectives include assessing the impact of these personalized TTs on hospitalization duration and the need for tube replacements. By leveraging routine imaging data, this approach seeks to streamline the integration of 3D printing into clinical practice without adding significant costs or additional diagnostic procedures. Design: A single center, randomized, prospective, controlled clinical trial will be conducted. Participants and Methods: 156 polytrauma patients requiring tracheostomy and meeting inclusion criteria will be randomized into two groups: one receiving customized TTs and the other receiving standard TTs. Patients will be followed from the immediate postoperative period during their hospital stay and up to six months post-surgical tracheostomy to monitor complications, hospital stay length, and the number of TT replacements. This comprehensive follow-up aims to capture both early and late complications, as well as assess recovery trajectories. Data will be analyzed using descriptive, bivariate, and multivariate methods to evaluate efficacy and potential confounders

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