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.
