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Characterization of dynamic compliance of the respiratory system in healthy anesthetized dogs
Journal article   Open access   Peer reviewed

Characterization of dynamic compliance of the respiratory system in healthy anesthetized dogs

Mathieu Raillard, Martina Mosing, Anthea Raisis, Adam Auckburally, Georgina Beaumont, Frances Downing, Charlotte Heselton, Paul MacFarlane, Karine Portier, Josephine Robertson, …
Frontiers in veterinary science, Vol.11, 1490494
2024
DOI: https://doi.org/10.3389/fvets.2024.1490494
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Published2.76 MBDownloadView
CC BY V4.0,  Open Access

Abstract

anesthesia compliance dogs dynamic compliance monitoring respiratory mechanics
Introduction: In clinical practice, evaluating dynamic compliance of the respiratory system (Cdyn) could provide valuable insights into respiratory mechanics. Reference values of Cdyn based on body weight have been reported, but various factors may affect them and the evidence is scanty. This study aimed to establish a reference interval for Cdyn and identify associated variables. Methods: Data were collected from 515 client-owned dogs requiring anesthesia, excluding those with lower airway disease. The dogs were anesthetized, the tracheas intubated, and lungs ventilated at clinicians' discretion across 11 centers in six countries, with no restrictions on anesthesia protocols or ventilation settings, except avoiding inspiratory pauses. Three Cdyn measurements from three consecutive breaths per dog were recorded using a standardized form, which also documented factors affecting Cdyn identified through literature and an online survey. Various spirometry technologies were used. The substantial variance in Cdyn measurements led to a comprehensive analysis using a multiple linear regression model. Multicollinearity (variables highly correlated with each other) was addressed by investigating, transforming, or excluding factors. Initial simple linear regression assessed each variable's individual effect on Cdyn, followed by a multiple linear regression model constructed via stepwise forward selection and backward elimination. Results: The best-fitting model identified a linear relationship between Cdyn and body mass when the following conditions were met: high BCS (Body Condition Score), orotracheal tubes <7% smaller than predicted, the use of a D-lite flow sensor, and the absence of a high FIO2 (>80%) exposure for more than 10 minutes before Cdyn measurement. In cases where these conditions were not met, additional factors needed to be incorporated into the model. Low (1/9, 2/9, 3/9) and medium (4/9, 5/9) BCS, an orotracheal tube of the predicted size or larger and longer inspiratory times were associated with increased Cdyn. The use of alternative spirometry sensors, including Ped-lite, or prolonged exposure to high FIO2 levels resulted in decreased Cdyn. Conclusion and clinical relevance: Establishing a reference interval for Cdyn proved challenging. A single reference interval may be misleading or unhelpful in clinical practice. Nevertheless, this study offers valuable insights into the factors affecting Cdyn in healthy anesthetized dogs, which should be considered in clinical assessments.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.154 Assisted Ventilation
1.154.277 Mechanical Ventilation
Web Of Science research areas
Veterinary Sciences
ESI research areas
Plant & Animal Science
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