Tracheostomy Tube Monitoring Accessory to Detect Accidental Decannulation and Obstruction Emergencies in Ventilator-Independent Pediatric Patients

Introduction: Some of the leading causes of tracheostomy-related complications in pediatric populations are accidental decannulation and obstruction events that result in permanent neurological damage or death if not mitigated quickly enough. Despite the severity of tracheostomy tube emergency events, there is currently no effective medical technology available that can specifically detect accidental decannulation or obstruction in a tracheostomized patient that is not ventilator dependent. Therefore, a pediatric tracheal breathing model system was developed to assist with testing new tracheostomy tube technologies for identifying emergency events. Methods: A custom carbon dioxide monitoring tracheostomy tube attachment was engineered to collect breathing waveform data during emergency events (e.g., improper insertion, accidental decannulation, and mucus obstruction). Anatomically accurate pediatric tracheal models for various age groups (0-3 months, 2-4 years, and 10-12 years old) were developed with modelling software and a 3D printer. A breathing simulator was integrated with the tracheal models to generate age-dependent respiration patterns during simulated tracheostomy tube emergencies. Results: Carbon dioxide readings from the custom tracheostomy tube attachment indicated distinct waveform recordings during simulated tracheostomy tube emergency events for all age groups tested. During incorrect insertion, accidental decannulation, and complete blockage of a tracheostomy tube, exhaled carbon dioxide readings remained static at ambient levels. Partial mucus obstruction of a tracheostomy tube decreased exhaled carbon dioxide waveform amplitude relative to unobstructed conditions. Conclusions: The tracheostomy tube attachment successfully recorded respiration patterns during simulated tracheostomy tube emergencies in pediatric patients of varying age. Breathing waveform data collected from the model system will aid in the development of emergency airway event detection software integrated in the tracheostomy tube sensing accessory.