Morphological specificity analysis of an image-based 3D model of airway filling in a difficult airway.

BMC Anesthesiol

Department of Anesthesiology, The First Affiliated Hospital of Kunming Medical University, No 295 Xichang Road, Wuhua District, Kunming City, Yunnan Province, 650032, People's Republic of China.

Published: November 2022

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Article Abstract

Background: The purpose of this study was to analyze position-specific morphological changes of the upper airway and to further assess the impact of these changes in difficult airway during intubation.

Methods: This observational comparative study included two groups (n = 20 patients/group): Group A had normal airway and Group B had difficult airway. Data obtained from two-dimensional magnetic resonance imaging were imported to Mimics V20.0 software for processing. We then reconstructed three-dimensional models of upper airway filling in patients in the supine and maximum extension position based on the imaging data. Those models were projected on coronal, sagittal, and horizontal planes to investigate multiple morphological features. We measured the surface area, radial length, and corner angle of the projected areas.

Results: Group A had larger upper airway filling volumes compared to Group B The volumes for the supine position were 6,323.83 ± 156.06 mm for Group A and 5,336.22 ± 316.13 mm for Group B (p = 0.003). The volumes the maximum extension position were 9,186.58 ± 512.61 mm for Group A and 6,735.46 ± 794.63 mm for Group B (p = 0.003). Airway volume increased in the upper airway filling model as the body position varied from the supine to maximum extension position (Group A: volume increase 2,953.75 ± 524.6 mm, rate of change 31%; Group B: volume increase 1,632.89 ± 662.66 mm, rate of change 25%; p = 0.052).

Conclusion: The three-dimensional reconstruction model developed in this study was used to digitally quantify morphological features of a difficult airway and could be used as a novel airway management assessment tool.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632020PMC
http://dx.doi.org/10.1186/s12871-022-01880-6DOI Listing

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