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TECHNOLOGY, COMPUTING, AND SIMULATION

Model-Based Detection of Endobronchial Intubation

Rachana K. Visaria, PhD, and Dwayne R. Westenskow, PhD^*

From the Departments of *Anesthesiology and Bioengineering, University of Utah, Salt Lake City, Utah.

Address correspondence and reprint requests to Dwayne R. Westenskow, PhD, Department of Anesthesiology, University of Utah, 3C 444 SOM, Salt Lake City, UT 84132. Address e-mail to Dwayne.Westenskow{at}hsc.utah.edu.

To detect endobronchial intubation (EBI) noninvasively in real time, we developed a novel, automated, lumped model-based approach. The model uses routinely monitored airway pressure and flow as inputs. The specificity of the method in detecting EBI was determined by testing events of stiff chest wall (SCW) in the absence of EBI. EBI was induced in 10 anesthetized, paralyzed, and mechanically ventilated mongrel dogs (19–45 kg) by advancing the endotracheal tube into the right mainstem bronchus. The event of SCW was created by wrapping a pressure cuff around the chest. Airway pressure and flow were continuously recorded at the mouth, and respiratory impedance was estimated from these signals. Model parameters were iteratively identified until the root mean square error between the respiratory and model-predicted impedance was minimum. The change in model parameters during EBI from baseline was analyzed. In nine of 10 cases, it was determined that during EBI, the model’s compliance element (C₁) decreased 50% and model’s resistance element (R₂) changed 10-fold from baseline. Testing this rule on 40 cases of SCW, four false positives were obtained. During SCW, R₁ and R₂ increased, whereas C₂ decreased significantly from baseline. This preliminary study is a promising step toward noninvasive, real-time detection of EBI to aid clinicians in decision making.

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