Evaluating the interaction of a tracheobronchial stent in an ovine in-vivo model
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Tracheobronchial stents are used to restore patency to stenosed airways. However, these devices are associated with many complications such as stent migration, granulation tissue formation, mucous plugging and stent strut fracture. Of these, granulation tissue formation is the complication that most frequently requires costly secondary interventions. In this study a biomechanical lung modelling framework recently developed by the authors to capture the lung in-vivo stress state under physiological loading is employed in conjunction with ovine pre-clinical stenting results and device experimental data to evaluate the effect of stent interaction on granulation tissue formation. Stenting is simulated using a validated model of a prototype covered laser-cut tracheobronchial stent in a semi-specific biomechanical lung model, and physiological loading is performed. Two computational methods are then used to predict possible granulation tissue formation: the standard method which utilises the increase in maximum principal stress change, and a newly proposed method which compares the change in contact pressure over a respiratory cycle. These computational predictions of granulation tissue formation are then compared to pre-clinical stenting observations after a 6-week implantation period. Experimental results of the pre-clinical stent implantation showed signs of granulation tissue formation both proximally and distally, with a greater proximal reaction. The standard method failed to show a correlation with the experimental results. However, the contact change method showed an apparent correlation with granulation tissue formation. These results suggest that this new method could be used as a tool to improve future device designs.
KeywordsBiomechanical Lung Tracheobronchial Nitinol Stenting Granulation Finite element method
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013 under Grant Agreement No. NMP3-SL-2012-280915)-PulmoStent. Funding from the College of Engineering and Informatics at NUI Galway through a College Scholarship is also acknowledged, along with funding support provided by the Structured PhD Programme in Biomedical Engineering and Regenerative Medicine (BMERM). Funded under the Programme for Research in Third-Level Institutions (PRTLI) Cycle 5 (Strand 2) and co-funded under the European Regional Development Fund (ERDF). The authors wish to acknowledge the SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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