Bates Lab

Projects

Tracheomalacia in Neonates

Neonates born prematurely suffer from many respiratory issues due to their underdeveloped lungs and airways. We aim to measure whether each patient’s disease is due to lung or airway issues by measuring the amount of energy the patient uses to move air through their trachea. For most patients, moving air through the trachea uses a very small number of calories, but if the trachea collapses, as in a disease called tracheomalacia, this energy increases and the patient does not have enough energy left over to grow and develop. The aim of this work is to provide clinicians with an understanding of which component of respiratory disease is causing each patient’s specific symptoms.

Improving Outcomes in Pediatric Obstructive Sleep Apnea (OSA) with Computational Fluid Dynamics

Children with complex obstructive sleep apnea (OSA) often undergo surgeries aimed at treating their airway collapse. We aim to improve the outcome of these surgeries be matching each patient to the surgical approach which is best suited to that specific patient. We analyze each patient’s airway anatomy, airway collapse, and airflow to understand why their airway collapses and how the muscles controlling structures like the tongue play a part by moving during breathing.

OSA is a common condition, affecting 2.2 million children in the USA alone. It is characterized as upper airway obstruction during sleep, which causes disrupted sleep and leads to developmental delay, cardiovascular complications, and impaired growth. Current surgical approaches to treat OSA do not always “cure” the patient, therefore the goal of this project is to create a computational tool to predict which surgical approach will provide the most successful outcome for each patient.

The goals of this project are to validate our computational model of the airway using phase contrast MRI of hyperpolarized xenon gas, identify characteristics of patients with OSA who benefit from different types of surgery, and to develop a predictive model of which treatments will work best for each patient.