Development of a Novel Diagnostic Modality for Upper Airway Obstruction via Integrating Dynamic Computed Tomography with Computational Fluid Dynamics

Project Summary/Abstract In the current state, clinical airway management decisions for patients with upper airway obstruction (UAO) are made based on a combination of qualitative imaging modalities, clinician experience, and a limited number of physiologic measures. Available diagnostics are inadequate and do not take into account the critical, dynamic

nature of the respiratory cycle. Our goal is to create patient specific, quantitative, data driven metrics for stratifying the severity of UAO for use in clinical decision making. The upper airway (nares to larynx) consists of dynamic structures that change throughout the respiratory cycle. Robin Sequence (RS) is a potentially fatal

congenital craniofacial condition characterized by undersized jaw, posteriorly displaced tongue, and resultant UAO. We chose to focus our initial efforts on RS patients due to the consistent clinical phenotype. Treatment options for patients with RS range from conservative measures such as prone positioning to invasive and

morbid surgical procedures. Clinical decisions for RS patients with UAO are made based on static imaging studies obtained at a random phase in the respiratory cycle, subjective interpretation of awake flexible airway endoscopy, blood CO2 levels, polysomnograms, the treatment team’s clinical impression of the patient’s

condition and the family’s goals of care. The factors contributing to clinical decisions are numerous but heavily influenced by multiple sources of bias based on the resources available at the treatment facility and the clinical training background/composition of the care team. There is a clear, unmet clinical need for a diagnostic

modality that can both characterize the anatomic narrowing and quantify UAO. We plan to address this need by integrating computational fluid dynamic (CFD) modeling with a novel computed tomography protocol that captures dynamic airway changes throughout the respiratory cycle (4D-CT). In the future state, we anticipate

the 4D-CT/CFD-based diagnostic modality will be used to define the anatomic location(s) of dynamic UAO and quantify the severity of the UAO at each level. This information will be used, in part, to determine which treatment is optimal for individual patients. As part of this work, we will define specific CFD and clinical

outcome measures that are most critical to UAO treatment decisions. Preliminary data shows that the CFD analysis output (breathing resistance, energy loss, peak velocity) can identify the level(s) and severity of airway compromise. The aims of this proposal are to further develop, refine and validate the technique for integrating

4D-CT acquisition with CFD analysis to ultimately inform treatment decisions in infants with UAO. By combining 4D-CT imaging with CFD techniques, we aim to create a simple, accurate, quantitative, patient specific diagnostic modality that will address the current gap. Once validated in RS patients this diagnostic

approach could be applied to numerous other conditions impacted by UAO including obstructive sleep apnea and laryngomalacia.