We seek to develop critical technology to help pre-mature infants and newborns in the first days of life survive and avoid serious airway complications that arise from subglottic stenosis (SGS), the most common acquired anomaly of the larynx in children and the most common abnormality requiring tracheotomy in infants. SGS is preventable and is caused by endotracheal intubation during the critical first days of life. In the premature infant and newborn, this condition may pass undetected or present as a life-threatening event. The ability to better treat and even prevent subglottic stenosis has improved with technical advances in neonatal intensive care, but diagnosis remains a source of continued controversy and debate. The tissues of the subglottis are delicate in nature, easily damaged, and rapidly develop edematous changes, and are predisposed to inflammation, scar formation, and stenosis. Surgical endoscopy has remained the gold standard in the diagnosis of SGS. Unfortunately, surgery requires general anesthesia and is limited to the characterization of the surface anatomy and does not provide detailed analysis of the sub-epithelial tissues. This clinical circumstance is often complicated as the pulmonary, cardiac, and hypoxic thresholds in the newborn population may postpone the evaluation of the airway due to limited tolerances to physiologic stress. In essence, the ultimate challenge in the evaluation of the newborn airway is to minimize diagnostic trauma and physiologic stress while accurately characterizing the laryngeal tissues. Optical Coherence Tomography (OCT) is an imaging modality that utilizes light to produce high-resolution images of living tissues with a resolution in excess of 10 5m. OCT allows for one to distinguish the epithelium from the underlying tissue microstructures based on tissue optical properties with real-time frame rates. Using OCT one can non-invasively characterize living tissues well beyond the current imaging capacities of MRI, CT and ultrasound. We propose to design and construct high speed, high resolution OCT technology combined with 3-D MEMs based probes to image the newborn and infant airways in the neonatal intensive care unit, and define the potential role of OCT in diagnosing the onset and progression of subglottic airway disease in these critically ill patients. First we will optimize the technology in a New Zealand white rabbit model of SGS and compare OCT with conventional microscopy. Then, we will image the subglottic airway in 200 intubated neonates at the UC Irvine-Children's of Hospital of Orange County ICUs and correlate morphologic and structural features obtained with OCT with physiologic and functional variables such as gestational age, weight, and number of failed extubations in order to identify variables that can be used to better predict successful airway extubation, monitor progression of pathophysiologic changes during intubation, and understand the in vivo growth and development of the neonatal airway. We believe we will be able to establish OCT as a viable diagnostic imaging modality that can be used to monitor the neonatal airway and diagnose the early the onset SGS and other ICU acquired laryngeal and upper airway diseases that lead to failed extubation and ultimately tracheostomy.
Subglottic stenosis is the most common indication for tracheostomy in critically ill neonates. Early diagnosis of disease onset may lead to reduction in the incidence of tracheostomy and other airway complications. Development of an office-based treatment technology to diagnosis subglottic stenosis or monitor its development would reduce the need for morbidity and decrease health care costs. Project Narrative Subglottic stenosis is the most common indication for tracheostomy in critically ill neonates. Early diagnosis of disease onset may lead to reduction in the incidence of tracheostomy and other airway complications. Development of an office-based treatment technology to diagnosis subglottic stenosis or monitor its development would reduce the need for morbidity and decrease health care costs.
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