During an obstructed endotracheal tube, model parameters change such that the event can be diagnosed noninvasively, automatically, and accurately. The model differentiates between upper airway obstruction and complications like bronchospasm and stiff chest wall.

No abstract

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 > or =50% and model's resistance element (R2) changed < or =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.

In the present study, a method was developed to diagnose most frequent and lethal intra-operative pulmonary complications -Bronchospasm, Obstructed endotracheal tube (OE'IT) and Pneumothorax. The method requires airway pressure and flow measured at the mouth. It is based on slope of expiratory flow and area under the Pressure-Volume (PV) loop. The method has been successfully tested on a mechanical simulator and animal model. In OETT, the expiratory slope decreases by 77%, whereas in Bronchospasm and Pneumothorax, the slope increases by 111 and 302 %, respectively. In Bronchospasm, the area of expiratory PV loop increases and that of inspiratory loop decreases. Whereas, in Pneumothorax the opposite happens and in OETT both areas increase. The increase in upper airway resistance in OETT, increase in lower airway resistance in Bronchospasm and decrease in lung compliance in Pneumothorax are believed to cause these differences in pressure and flow behavior. The algorithm is a potential and simplistic approach to partition the upper and lower airway resistance as compared to the already existing complicated lumped and distributed models. Since the analysis is real time and requires minimum noninvasive monitoring, it will aid the clinicians to correctly, quickly and easily diagnose the pulmonary abnormalities.