Jeffrey Hwang

Abstract

Introduction: Obstructive sleep apnea (OSA) is a common chronic disorder that is characterized by repetitive episodes of airflow cessation or reduction occurring during sleep as a result of partial or complete upper airway obstruction. These recurrent events have a tremendous impact on the cardiovascular system with a multitude of dangerous consequences. Numerous studies have been conducted determining etiological risk factors for OSA including anatomical predictors which have been observed with multiple imaging techniques. Cone beam computed tomography (CBCT) is a low-radiation mode of imaging that can be used to accurately identify anatomical landmarks and measure craniofacial relationships and airway dimensions. The purpose of this study was to determine if CBCT parameters can be used to create a prediction model for OSA. The model was formulated based on statistical analysis of the most predictive CBCT-based parameters, BMl, and scores from a sleep questionnaire.

Methods and Materials: Forty-nine CBCT measurements from 39 non-growing patients with OSA were analyzed to create a prediction model for the severity of OSA. In addition, composite scores from a sleep questionnaire were calculated and were analyzed for our model along with age, gender, and BMl.

Results: Spearman rank (ρ) correlations were carried out between the natural logarithm of RDI (InRDI) and CBCT measurements, age, BMI, and SDQ scores by gender. For males. Body Mass Index (BMI), AW-Go-T and AW-PNS-S were significant predictors of InRDI, F (3, 20) = 9.66, p = .001. The regression of InRDI on BMI, AW-Go-T, and AW-PNS-S resulted in the following regression equation, Y' = 0.075 (BMI) - .054 (AWGo- T) + 0.056 (AW-PNS-S) + 1.279; R2 = .630. For females, SDQ, Nar2S and Nar1T/Apex-E-V were significant predictors of InRDI, F (3, 16) = 23.11, p < .001. The regression of InRDI on SDQ, Nar2S and Nar1T/Apex-E-V resulted in the following regression equation, Y' = .068 (SDQ) - .088 (Nar2S) - 1344.52 (Nar1T/Apex-E-V) + 4.19; R2=.850.

Conclusions: The visualization of craniofacial, soft tissue and airway parameters offered by CBCT is advantageous for assessing risk factors for OSA and identifying patients that may need further diagnosis for sleep-related disorders. In our study, separate gender specific predictive models were created which performed better than a single model. The variables incorporated into the models are best measured from CBCT and cannot be visualized by any 2D cephalometric radiograph. The predictive models we developed may be used by orthodontists and other health care professionals to identify patients at risk of OSA who would benefit from further diagnosis.

LLU Discipline

Orthodontics and Dentofacial Orthopedics

Department

Orthodontics and Dentofacial Orthopedics

School

Graduate Studies

First Advisor

Joseph M. Caruso

Second Advisor

James R. Farrage

Third Advisor

R. David Rynearson

Degree Name

Master of Science (MS)

Degree Level

M.S.

Year Degree Awarded

2010

Date (Title Page)

9-2010

Language

English

Library of Congress/MESH Subject Headings

Orthodontics, Corrective; Orthodontic Appliances, Functional; Airway Obstruction; Sleep Apnea Syndromes; Sleep Apnea, Obstructive -- therapy; Cephalometry -- methods; Mandibular Advancement -- instrumentation; Cone-Beam Computed Tomography; Sex Factors; Predictive Value of Tests

Type

Thesis

Page Count

xv; 64

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

Download

Share

COinS