Jodi Parker

Abstract

Introduction: Obstructive Sleep Apnea (OSA) patients have increased risk of morbidity and mortality. Early diagnosis may reduce morbidity and mortality. Prediction of OSA from imaging may help to identify OSA patients earlier in life. CBCT can be used for OSA diagnostic imaging due to its three-dimensional (3D) visualization of the upper airway and craniofacial complex. Magnification associated with conventional 2D radiography is eliminated with CBCT, and radiation to the patient is significantly less than previous modalities used to measure craniofacial & airway measurements associated with OSA. During a CBCT scan, the patient's image is taken supine, rather than the upright position often necessary for conventional two-dimensional (2D) radiography. CBCT imaging is routinely used for diagnosis and treatment planning in the Loma Linda University Department of Orthodontics and Dentofacial Orthopedics. If a simple prediction model were available for the prediction of RDl, it may be possible to identify patients at higher risk for OSA and refer them earlier in life. The purpose of this study was to identify the most predictive craniofacial and airway parameters for RDl and create a prediction model to identify patients at higher risk for OSA.

Methods and Materials: Fifty consecutive patients referred to the Loma Linda University Medical Center Sleep Disorders Center, age 4 to 65 years, were included in the study. Each patient had a Polysomnography evaluation and a CBCT scan administered. Researchers were blinded to RDI and CBCT measurements. Forty-one CBCT measurements, previously reported in the literature to have a relationship with OSA, were analyzed for the purpose of creating an accurate prediction model for predicting RDI. All craniofacial, soft tissue and airway measurements made from the CBCT were measured by the author and evaluated by a radiologist for any pathological incidental findings.

Results: After reducing the sample size to 25 subjects for a more reasonably powered study, a univariate analysis identified 12 parameters with a statistically significant relationship to RDI, including: Nar2T, MdW, MP-H, PL, AW-Go-S, Apex-E-V, AW-ES, AW-E-T, PNS-E-A, Apex-E-A, Cd-Gn, OxsA. A multivariate analysis, controlling for all variables evaluated by the univariate analysis, did not find any parameters as independent predictors of increasing RDI. It was suspected that some variables may be too highly correlated with one another and this may affect their statistical significance in the model. A collinearity analysis was conducted to determine if any variables were correlated with each other so strongly that they canceled each other out of the model. This analysis identified 5 variables, Cd-Gn, MdW, OxsA, Apex-E-A and PNS-E-A, as having such a strong correlation with the other variables that they canceled them out of the model. These variables were eliminated from the multivariate analysis and it was conducted again. This second multivariate analysis identified only one parameter with a statistically significant (p < 0.05) relationship to RDI. Total airway volume was found to be the only independent predictor for increasing RDIin [sic] our model. To be certain that collinearity was not still a factor, the collinearity test was conducted again and it was determined that no variable was correlated so highly with another that it was necessary to eliminate it from the multivariate analysis. Using linear regression, the following equation was formulated based on the predictive factor identified as statistically significant for RDl from the CBCT scan: RDI = 3.696 + 0.003(Apex-E-V).

Conclusions: Evaluating craniofacial, soft tissue and airway parameters with a relationship to OSA and its severity, assessed by RDI, for subjects aged 4-65 years, using CBCT imaging, can be a more effective diagnostic tool than cephalometric radiography. This is due to the 3D visualization and volume manipulation possible with CBCT images. The most predictive variable identified by this study, total airway volume, cannot be visualized by a 2D cephalometric radiograph. Incorporating total airway volume into a simple prediction model, RDI = 3.696 + ((0.003)(Total Airway Volume)), allows orthodontists and other health care professionals to accurately determine which patients would benefit from a referral to an American Academy of Sleep Medicine accredited Sleep Disorder Center for evaluation for OSA, using the latest and safest technology.

LLU Discipline

Orthodontics and Dentofacial Orthopedics

Department

Orthodontics and Dentofacial Orthopedics

School

Graduate Studies

First Advisor

Joseph M. Caruso

Second Advisor

Ralph Downey III

Third Advisor

James R. Farrage

Fourth Advisor

R. David Rynearson

Degree Name

Master of Science (MS)

Degree Level

M.S.

Year Degree Awarded

2009

Date (Title Page)

9-2009

Language

English

Library of Congress/MESH Subject Headings

Orthodontics, Interceptive; Cephalometry -- methods; Airway Obstruction; Sleep Apnea Syndromes; Sleep Apnea, Obstructive -- diagnosis; Palate, Soft -- radiography; Pharynx -- radiography; Cone-Beam Computed Tomography; Imaging, Three-Dimensional; Case-Control Studies; Predictive Value of Tests

Type

Thesis

Page Count

xvi; 91

Digital Format

PDF

Digital Publisher

Loma Linda University Libraries

Usage Rights

This title appears here courtesy of the author, who has granted Loma Linda University a limited, non-exclusive right to make this publication available to the public. The author retains all other copyrights.

Collection

Loma Linda University Electronic Theses and Dissertations

Collection Website

http://scholarsrepository.llu.edu/etd/

Repository

Loma Linda University. Del E. Webb Memorial Library. University Archives

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