Evaluation of Submandibular Infections Using 3-Dimensional Reconstruction of Computed Tomography Images

Evaluation of Submandibular Infections using 3-Dimensional Reconstruction of Computed Tomography Images

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Hashim G. Elmshiti

Graduate Program in Anatomy

The Ohio State University

2016

Dissertation Committee: Professor Kirk McHugh, Advisor Assistant Professor Jennifer Burgoon Assistant Professor Eileen Kalmar Professor Gregory Ness Assistant Professor Luciano Prevedello

Hashim G. Elmshiti

2016

Abstract

Purpose

This study presents the use of three-dimensional computed tomography (3D-CT) in the evaluation of odontogenic maxillofacial infections. A secondary surgical procedure is common amongst maxillofacial infected patients to establish drainage of an abscess.

These secondary procedures lead to an increase in hospital length of stay (LOS), which creates a problem for the surgeons, patients, and insurance companies. In this study, we examined the following variables: second surgery (SS), and increasing hospital LOS as the outcome variables, and the other clinical and three-dimensional (3D) factors as predictive variables. This retrospective study will determine if there is a relationship between the predictive and outcome variables.

Study Design

This imaging project was approved by the Institutional Review Board (IRB) using

103 deidentified patient scans. Patient records were collected from the Wexner Medical

Center at The Ohio State University by the Division of Maxillofacial Surgery between

November, 2011 and September, 2014. Patient ages included in the study ranged from 18 to 73 years. The proposed analytic method is divided into two basic approaches: clinical data collection and 3D data collection. In addition to the computed tomography images, the clinical variables recorded from the patient‘s chart were age, sex, race, ethnicity, ii

etiology, number of surgical interventions, past medical history (ie. hypertension, diabetes, cancer, and renal insufficiency) and time of admission, operation and discharge.

The 3D image variables examined included the volume of submandibular infection, volume of associated muscles, and spatial relationship of 3D polygonal shape of the infection to adjacent structures. Moreover, we selected the following three clinical variables as outcome variables: Second Surgical Procedures (SS), and Hospital length of stay (LOS). The other remaing clinical variables (demographic, autoimmune suppression diseases, infected facial spaces, time frame variables and etc) and 3D variables are predictive variables.

Results:

All of the participants included in this study were clinically diagnosed with a submandibular space infection with or without extension to other facial spaces.

The predictive variables that were found to be significantly associated with second surgery (SS): retromolar trigon region (p = 0.021), and submental space (p = 0.040). By comparison, the predictive variables that were found to be significantly associated with

LOS included age (p = 0.031), submental space (p = 0.022), and air-way narrowing (p =

0.035). 3D-CT reformatted images showed a measurable difference in volume between the masseter (3DIM) (t = 7.300, p = 0.001) and medial pterygoid (3DIMP) (t = 6.390, p =

0.001) muscles on the infected versus uninfected side, while the volume of the lateral pterygoid (3DILP) remained statistically unchanged. iii

Conclusions:

The difference in volume between the muscles on the affected side and the unaffected side appear to represent a good predictive measure for the severity of infection. However, when we used to predict the severity of infections, can not show any significant statistical relationship with the outcome variables.

Although most of the health professional who treat patients with the odontogenic infections correlated the size of swelling with the severity of infection, 3D volume of the submandibular space infection can not show any significant relationship with the second operation and Hospital LOS, which inturn could not predict the sverity of the submandibular space infections. Using 3D-CT in evaluating submandibular space infection can provide a better orientation and understanding of the anatomical relationship between the infection and adjacent structures, which can‘t be easily shown in

2D-CT. This study illustrates that the computed tomography images and 3D-CT can assist in diagnosing and presurgical planning for the treatment of submandibular space infections and may prove useful in the treatment of other deep neck facial space infections.

Dedication

This document is dedicated to my family.

Acknowledgments

I have no words that can express my thanks for everyone who helped and assisted me during the preparation for the PhD in Anatomy. I am fully indebted to Dr. McHugh, my advisor, for his patience and continuous support to help achieve my goal. My deep gratitude to Professor Gregory Ness who assisted me greatly in brainstorming the ideas of this dissertation; moreover, I greatly appreciate Dr. Luciano Prevedello who provided assistance in the 3-D segmentation portion of the dissertation. Also, I extend my appreciation to Dr. Jennifer Burgoon and Dr. Eileen Kalamar for their assistance and suggestions throughout my dissertation. Thanks also extend to the past members who taught me the different courses of the Anatomy including Dr. Kenneth Jones, Dr. Doug

Gould, and Dr. Lisa Lee. I also thank the staff including Melody Barton, Mark Whitmer, and Michelle Whitmer in the Division of Anatomy. Thanks also extend to Dr. Robert

DePhilip, Dr. Jahanzeb Chaudhry, and Darlene who supported me in the early stages of this dissertation. I also thank Ryan Ziegler, Data Analyst at the department of biomedical informatics, for providing the information and CT images of different participants.

Moreover, I greatly appreciate Dr. Mark Hubbe for his statistical consultation. I especially thank my mom for her prayer to finish my study. Last but not least, I would like to acknowledge my wife and sons who encourage and kept me going up to the end point.

Vita

August 1980 …………...... Elfateh high school, Elbeida, Libya

1986...... B.D.S. in Dentistry, Garyounis University,

Benghazi, Libya

1986 to 2002 ...... General Dental Practitioner, Central Dental

Clinic, Benghazi, Libya

2002…...... MSc. in Anatomy and Embryology,

Garyounis University, Benghazi, Libya

2002 to 2007 ...... Lecturer, Division of Anatomy, Medical

School, Garyounis University, Benghazi,

Libya

July 2012…………………………………. M.S. Anatomy, The Ohio State University,

Columbus, Ohio

2012 to present ...... PhD Student, Division of Anatomy, The

Ohio State University, Columbus, Ohio

Fields of Study

Major Field: Anatomy

vii

Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgments………………………………………………………………………..vi

Vita ...... vii

Table of Contents ...... viii

List of Tables ...... xiv

List of Figures...... xvi

Chapter 1: Introduction & Literature Review……………………………………………..1

1.1 Overview of Study……………………………………………………………….....1

1.2 Background of the problem and Significance of the study………………………....3

1.2.1 Problem statement……………………………………………………………….3

1.2.2 Head and Neck infection………………………………………………………...4

1.2.3 Odontogenic infection…………………………………………………………...5

1.2.4 Overview of natural progression (primary to secondary spaces to remote spread)

………………………………………………………………………………………....6

1.2.5 Characteristics of infection (abscess vs. cellulitis, ability to spread)…………...8 viii

1.3 Significance…………………………………………………………………..……..8

1.3.1 Increase fundamental understanding of natural history of this condition……8

1.3.2 Evaluate utility of technique for clinical diagnosis and treatment planning…9

1.4 Normal Anatomy…………………………………………………………….…….12

1.4.1 Head and Neck Fascia……………………………………………………..…12

1.4.2 Head and neck fascial spaces……………………………………………..….14

A. Superficial Spaces……………………………………………………………14

B. Deep Spaces……………………………………………………………….….15

I. Spaces around the mandible………………………………………………...…15

II. Spaces running through the entire length of the neck…………………..……17

1.5 Maxillofacial Infections and Diagnosis…………………………………………...21

1.6 Standard Clinical Imaging ……………………………………………………….22

1.7 Techniques for 3D Reconstruction……………………………………………….24

1.8 Osirix (3D open-source software)………………………………………………..25

1.9 Potential Novel Applications of 3D images………………………………….…..26

1.10 Research questions……………………………………………………………...27

1.11 Purpose of the study………………………………………………………….....27

1.12 Hypotheses……………………………………………………………………..27

1.12.1 Hypothesis I…………………………………………………………….27

1.12.2 Hypothesis II……………………………………………………………28 ix

1.12.3 Hypothesis III…………………………………………………………..28

1.12.4 Hypothesis IV…………………………………………………………..29

1.13 Aims of this Study………………………………………………………..30

Chapter 2: Research Design and Methods……………………………………………….31

2.1 Project Design……………………………………………………………………31

2.1.1 Clinical Variables Collection……………………………………………….35

2.1.1.1 Participants…………………………………………………………..35

2.1.1.2 Demographic Variables…………………………………………...…35

2.1.1.3 Systemic Diseases……………………………………………………36

2.1.1.4 Time-Frame Variables……………………………………………….36

2.1.1.5 Etiology of Infections………………………………………….…….36

2.1.1.6 Anatomical Variables………………………………………………..36

2.1.1.7 Characteristics of Variables (Abscess vs Cellulitis)………………....37

2.1.1.8 Consequencies of infection…………………………………………..38

2.1.2 Three-Dimensional Variables……………………………………………….39

2.1.2.1 Volume of the Infection……………………………………………...39

2.1.2.2 Shape of the Infection………………………………………………..40

2.1.2.3 Spatial Relationship………………………………………………….40

2.1.2.4 Fluid Density…………………………………………………………41

2.1.2.5 Volume of Involved Muscles………………………………………..41

2.2 Instruments…………………………………………………………………….….41

2.2.1 Computed Tomography (CT) Images ………………………………….…..41

2.2.2 OsiriX ………………………………………………………………………42

2.2.2.1 Definitions of different modes……………………………………….43

2.3 Procedures………………………………………………………………………...44

2.3.1 Steps for ROI Segmentation with OsiriX Using a Manual Segmenting

Technique…………………………………………………………………………44

2.3.2 Steps for Muscle Segmentation with Osirix………………………………..45

2.3.3 Steps for Cropping 3D Volume with Osirix…………………………….….46

2.4 Methods of Analysis………………………………………………………………46

Chapter 3: Results……………………………………………………………………….58

3.1 Data Analysis……………………………………………………………………..58

3.2. Descriptive Statistics……………………………………………………………..59

3.2.1 Clinical Study…………………………………………………………………59

3.2.1.1 Demographic Variables…………………………………………………...59

3.2.1.2 Time-Related Variables…………………………………………………...62

3.2.1.3 Anatomical Location…………………………………………………..…64

3.2.2 Three dimensional study……………………………………………………...67

3.2.2.1 Measurements of 3D Associated Muscles…………………………………67 xi

3.2.2.2 Measurements of Region of Interest (ROI)………………………………68

3.2.2.3 Comparison between 3D Volumes of Infected and Non-Infected

Muscles…………………………………………………………………………….68

3.3. Analytic Statistics………………………………………………………………..72

3.3.1. Second Surgery (SS)…………………………………………………………73

3.3.1.2 3D Variables vs Second Surgery………………………………….…….74

3.3.2 Hospital LOS………………………………………………………………….80

3.3.2.1 Hospital LOS vs 3D Variables……………………………………………86

3.4 3D-CT Spatial Relationship of Submandibular Space Infections and Adjacent

Structures……………………………………………………………………………....88

Chapter 4: Discussion ………………………………………………………………….....97

4.1 Clinical Data Investigation……………………………………………………………98

4.2 Anatomical Location and Extent of Infection……………………………………....102

4.3 Facial Space Infection……………………………………………………………....103

4.4 Consequencies of the submandibular space infection………………………….....114

4.5 3D Volume of Submandibular infections…………………………………………....119

4.6 3D Muscular Relationships……………………………………………………….....120

4.7 Spatial Relationship of ROI with Adjacent Area…………………………………...121

Limitations………………………………………………………………………….……125

Chapter 5: Conclusions………………………………………………………………….127 xii

Summary………………………………………………………………………………...128

References ...... 129

Appendix A: Raw Data - Demographic ...... 143

Appendix B: Descriptive Statistics ...... 164

Appendix C: Analytic Statistics – Second surgery ...... 214

Appendix D: Analytic statistics- LOS ...... 248

Appendix E: LOS Plots ...... 278

Appendix F: Spatial Relationship ...... 286

xiii

List of Tables

Table 1. Sample of different variables that have been selected for this project ...... 34

Table 2. Severity scores for sever odontogenic infection...... 38

Table 3. Demographic variables of the study group...... 60

Table 4. Systemic diseases associated with the study group...... 60

Table 5. Consquencies of submandibular infections...... 62

Table 6. Time interval between admission, time of first surgery, and discharge...... 63

Table 7. Time interval between admission, time of second surgery, and discharge...... 66

Table 8. Anatomical distribution of infections...... 66

Table 9. Character of infections...... 70

Table 10. Measurements of affected and non-affected muscles...... 71

Table 11. Measurement of ROIs volume and Fluid density...... 71

Table 12. Three dimensional affected muscles vs three dimensional non-affected muscles

……………………………………………………………………………………………71

Table 13. Hypothesis results...... 72

Table 14 . Relationships between gender, race, and second operation...... 75

Table 15. Relationships between the independent variables and dependent variable second surgery...... 76

xiv

Table 16. Relationship between facial spaces around the mandible and the second operation ...... 76

Table 17. Relationships of facial spaces along the entire length of the neck with the

Second Surgery...... 77

Table 18. Relationship between infection's characters and second surgery...... 78

Table 19. Relationship between 3D variables and second surgery ...... 78

Table 20. Hypothesis results in predictions of second surgery...... 79

Table 21. Relationships between Gender, Race, and LOS...... 81

Table 22. Relationships between systemic disease, and LOS...... 82

Table 23. Relationship between the spaces located around the mandible and hospital

LOS ...... 83

Table 24. Relationship between the spaces running along the entire length of the neck and hospital LOS...... 84

Table 25. Relationships between infection's characters and hospital LOS...... 85

Table 26. Relationship between 3D independent variables and LOS dependent variable.

...... 87

Table 27. Hypothesis results in predictions of LOS...... 87

List of Figures

Figure 1. The different maxillofacial spaces………………………………………………4

Figure 2. Relationships of the tooth apex and muscle attachment in the maxillary and mandibular region ...... 7

Figure 3. The different facial spaces in cross and horizontal sections...... 11

Figure 4. Shows the retropharyngeal space (blue) and danger space (red) ...... 20

Figure 5. Design process of study showing the different clinical and 3D variables...... 33

Figure 6. The diagram illustrates the shape and volume of the left submandibular infection...... 47

Figure 7. Other polygonal shape of the submandibular space infection...... 47

Figure 8. 3D ROI of the submandibular infection after relocated on its anatomical position...... 48

Figure 9. Diagram shows 3D image after cropping...... 49

Figure 10. Highlighting of the right submandibular space infection...... 50

Figure 11. Highlighting of two separate attenuation areas represented sublingual and submandibular infections...... 51

Figure 12. 2D image shows highlighting muscles on both affected and non-affected side.

...... 52

Figure 13. Illustrates highlighting the first slice of left masseter muscle and right submandibular infection...... 53 xvi

Figure 14. 3D volume rendering image shows the external appearance of the right submandibular infection...... 54

Figure 15. Illustrates the relationship between the 3D submandibular space infection, the masseter, and the mylohyoid muscel...... 55

Figure 16. Demonstrates different anatomical structures related to the polygonal shape of right submandibular infection...... 56

Figure 17. Illustrates the spatial relationship of the polygonal shape of infection to the mandible and blood vessels...... 57

Figure 18. This diagram demonstrates the distribution of deep facial space infection among different decades...... 61

Figure 19. 3D polygon of left masticator space infection and associated muscles

(masseter & medial pterygoid)...... 92

Figure 20. 3D-CT images show the relationships of polygonal shape of right submandibular infection to the adjacent structures...... 93

Figure 21. 3D-CT images illustrate the relationship of right submandibular space infection to the adjacent soft and hard tissues...... 94

Figure 22. 3D-CT images show polygonal shape of sever right submandibular infection across the midline and extends to the left side...... 95

Figure 23. 3D-CT images demonstrate the spatial relationship of the polygonal shape of the submandibular space infection and the adjacent structures...... 96 xvii

Figure 24. Distribution of males and females among maxillofacial infection………….100

Figure 27. The diagram demonstrates the distribution of different facial spaces among the severty of infection……………………………………………………………………..104

Figure 28. The diagram illustrates the left submandibular space infection at angle of the mandible………………………………………………………………………………...104

Figure 29. CT image demonstrates the sublingual and parapharyngeal spaces infections and direct communication with the submandibular space…………………………….105

Figure 30. Axial CT image shows collection of fluid within the right parapharyngeal space…………………………………………………………………………………….110

Figure 31. Axial CT image shows the parapharyngeal abscess in communication with the submandibular infection with displacement of the air way…………………………….110

Figure 32. Axial CT reveals a masticator space abscess...... 111

xviii

Chapter 1: Introduction & Literature Review

1.1 Overview of Study

Maxillofacial infection is one of the most common sources of head and neck infections and can lead to a fatal condition when it gains access to the deep facial spaces that are associated with vital structures (Wang, 1999). Due to the unpredictability of odontogenic infections, more studies are needed to properly evaluate the spread of the infection, associated complications, hospital length of stay (LOS), and hospital costs.

The factors that affect the hospital LOS have not been completely investigated with limited studies examining the different variables that contribute to the variation in this important factor (Peters et al., 1996). In this study, I hypothesize that the 3D reconstruction of CT images will aid in identifying the key factors that are associated with severity of infection and increased hospital LOS.

This dissertation will describe the application of the three-dimensional (3D) reconstruction of CT images in predicting the outcome of the submandibular space infection. Using hospital LOS and need for second operation as proxies for severity. 3D modeling provides realistic anatomic images that can be mapped to the same anatomical position on the patient. Moreover, addition of the third dimensional axis allows data navigation and cropping of the image to get direct assessment of hidden parts.

To this end, we examined 103 patient CT‘s and perform 3-D reconstruction of their submandibular infections. These results were compared to a variety of clinical data 1

including: age, sex, demographic, morbidity, anatomical location and extension, complications, type of infection (abscess vs cellulitis), 3D volume of infection, 3D shape of infection, and 3D volume of associated muscles to determine which factors can predict clinical outcomes. The aid of this study is to identify predictors of secondary operations, and hospital LOS. To this end, it is specifically propose to:

1. Perform 3D-CT image analysis of submandibular infections to determine if abcess volume is correlated with severity of infection and treatment outcome.

2. Perform 3D-CT image analysis of submandibular infections to determine if abcess shape is correlated with severity of infection and treatment outcome.

3. Perform 3D-CT image analysis on the muscles of mastication to determine if individual muscle volume is correlated with severity of infection and treatment outcome

4. Determine which, if any, clinical variables including [demographic, systemic diseases, and fascial spaces variables ] are correlated with severity of infection and treatment outcome

1.2 Background of the Problem and Significance of the Study

The outcome of an infection arising from the teeth or teeth supporting structures can vary significantly. Some patients may respond very well to medical treatment, others may need both surgical intervention and medical treatment (Ohshima et al., 2009).

However, the severity and unexpected course of some maxillofacial infections may lead to more than one surgical intervention, which in turn increases hospital LOS and cost more money.

Some investigators reported that the relationship between the low rate of insurance coverage and costs involved in treatment of odontogenic infections have a negative impact in maintaining high standards of care (Jundt & Gutta, 2012; Peters et al.,

1996). In addition, Larawin et al. (2006) reported a low level of morbidity associated with patients that delayed treatment due to financial constraints. Clearly, additional studies are needed to properly evaluate the surgical intervention outcome, LOS, and cost .

1.2.1 Problem Statement

Some patients respond well to the first surgical intervention, while other patients need more than one surgical procedure significantly increasing their hospital LOS and overall costs for treatment (Kim et al., 2012).The goal of this project is to identify new diagnostic correlates that better predict the clinical outcome of maxillofacial infections.

Figure 1. The different maxillofacial spaces. [Image as presented by Girn, J. & Jo, C. (2008). Ludwig’s Angina. In S. Bagheri & C. Jo (Eds.), Clinical review of oral and maxillofacial surgery. St. Louis, MO: Mosby, p. 71.]

1.2.2 Head and Neck Infection

Infections are complex processes induced by many factors. The main factors are virulence of the microorganisms and the immune system of the host (Salam, 2008).

Infections of the deep head and neck spaces may involve different types of infections such as those affecting the salivary glands, the epiglottis, the tonsils, or the retropharynx.

Dentoalveolar abscesses are the most common cause of the deep fascial infections

(Hamza et al., 2003).

1.2.3 Odontogenic Infection

Maxillofacial infections may be produced by a large number of bacteria located in different parts of the oral cavity – the mucous membrane, the dental fissures and pits, and within the cervicular sulci. These infections are particularly dangerous because they can spread along the facial planes from their point of origin to deeply situated spaces in the head and neck (Figure 1). These infections have a variety of origins, including;

1. Deep periodontal pockets, such as those occurring in periodontal abscesses and in

operculum pericoronitis,

2. Dental caries through which the bacteria gain access to the periapical tissues via

pulpal tissues and lead to periapical periodontitis and subsequently spread to the

bone, which can induce osteomyelitis and soft tissues cellulitis in the neighboring soft

tissues,

3. Traumatic surgical injury or surgical interventions.

Odontogenic infections arise from either the tooth apex or periodontal pockets

(Figure 2). Once bacteria collect within the alveolar bone, they can perforate the thinnest part of the lingual cortical bone and gain access to the deep facial spaces becoming difficult to evaluate by clinical and conventional radiological procedures (Ariji et al.,

2002). As a result, infections may take the form of cellulitis or an abscess. The spread of infections to the deep facial spaces depends in part on the relationships of the tooth apex and the mylohyoid muscle. Infections of the lower second and third molar teeth tend to 5

pass into the submandibular space because their root apices lie below the origin of the mylohyoid muscle, while the rest of the lower teeth tend to pass to the sublingual space because their root apices lie above the level of mylohyoid muscle (Figure 2) (Lypka &

Hammoudeh, 2011).

Dental infections break down the barriers between the fascial spaces that are formed by the deep cervical fascia. Although the risk of odontogenic infections can be reduced by using antibiotics, the spread of the infection into the deep spaces of the head and neck may eventually involve the mediastinum, pleuropulmonary spaces, and heart valves and can lead to serious and even life threatening conditions (Yonetsu et al., 1998).

Ylijoki et al., (2001) tried to evaluate the signs and symptoms of odontogenic infections by making comparisons between the patients who admitted to the intensive care unit

(ICU) and non-ICU patients. However, their results did not support their hypothesis, for example, hospital LOS did not correlate with infection severity, and they explained these results based upon the small number of patients included in the study.

1.2.4 Overview of Natural Progression (Primary to Secondary Spaces to Remote Spread)

The most common infections associated with head and neck are those that are correlated with odontogenic infections, and among the facial spaces, the submandibular facial space is the most common area involved by dental infections of the lower posterior

teeth. The spread of infection to the submandibular space may also result from sublingual or masticatory space infections.

Severe submandibular infections are always hard to evaluate due to the complex nature of facial planes (Figures 1 & 2). The infections may spread to parapharyngeal, carotid, and retropharyngeal spaces that in turn can lead to airway obstruction (Ariji et al., 2002).

Misinterpretation of signs and symptoms may result in life threatening conditions such as fatal mediastinitis, or jugular vein thrombosis. (Kato et al., 2001; Shahbazian et al.,

2013).

Figure 2. Relationships of the tooth apex and muscle attachment in the maxillary and mandibular region (From Goldberg MH, Topazian RG., 1987 p. 170).

1.2.5 Characteristics of Infection (Abscess vs. Cellulitis, Ability to Spread)

Odontogenic infections can be categorized as cellulitis or an abscess. Cellulitis in the facial spaces is an infection of the adipose tissue (de Vicente Rodriguez, 2004). An odontogenic abscess is more complex because it can involve several facial spaces. When odontogenic infections involve the retropharyngeal space, it is often difficult for the clinician to differentiate between the cellulitis and an abscess. Thus, the planning of surgical intervention is difficult (Rosenthal et al., 2011).

While any infection can be life threatening, infections of the head and neck facial spaces are considered particularly dangerous because they may spread and lead to compression of the airway, mediastinitis in the chest, or an infection of the heart valves

(Reynolds & Chow, 2007). Numerous investigators have reported many complications from odontogenic infections (de Vicente Rodriguez, 2004; Michael & Jeffrey, 2011).

Orbital infections can lead to blindness or intracranial cavernous sinus thrombosis.

Necrotizing fasciitis is an infection of the superficial fascia that can be accompanied by the necrosis of large areas of skin. Mortality due to cervical necrotizing fasciitis has been reported to be as high as 40% (Hamza et al., 2003).

1.3 Significance

1.3.1 Increase Fundamental Understanding of Natural History of this Condition

The spreading of odontogenic infection to the submandibular space is considered clinically significant because of the frequent involvement of other facial spaces (Ariji et 8

al., 2002). Deep facial space infections are life-threatening conditions due to their complications such as airway compression, descending mediastinitis, pericarditis, venous thrombosis, artery rupture, and cavernous sinus thrombosis. These complications are important in the design of treatment plans in order to avoid the risk of death.

1.3.2 Evaluate Utility of Technique for Clinical Diagnosis and Treatment Planning

Diagnosis and surgical treatment of maxillofacial infections are clinically complicated because they depend on many factors such as the virulence of microorganisms, the health status of body‘s immune system, and the anatomical area involved (de Vicente Rodriguez, 2004). These reasons make the accurate diagnosis maxillofacial infections important. Advanced imaging techniques such as CT, have been used in the assessment and handling of head and neck deep fascial space infections. The decision-making proceses of maxillofacial surgeons frequently depends on clinical examination as well as information revealed by imaging studies (Hassfeld et al., 2003).

Three-dimensional images reformatted from CT scans have been shown to provide additional information that can help in the diagnosis and management of maxillofacial infections (Reuben et al., 2005).

Even though the widespread use of antibiotics and contemporary dental care have decreased the incidence of serious maxillofacial infections, they remain clinically significant because they have the potential to generate serious life threatening complications. To get an accurate diagnosis and evaluation, surgeons need to evaluate the 9

degree of infection and its spatial involvement when planning for surgical intervention.

We propose that 3D-CT images of submandibular space infections may exhibit factors useful as predictors for the severity of infection. Distribution of such factors may include size, morphology, involved spaces, and changes in adjacent tissues such as increased muscle volume, segmentation or multiple locule formations.

1.4 Normal Anatomy

1.4.1 Head and Neck Fascia

Proper understanding of the anatomy of the cervical fascia can facilitate understanding the location and extent of head and neck infections, help predict the outcome of surgical drainage and guide the accurate treatment of cervical infections. The different divisions of cervical fascia blend in a fashion that leads to the formation of distinct facial spaces and clefts (Figure 3). In addition, the various facial planes can act as barriers to the spread of the infection as well as direct the path that the infection will take.

Unfortunately, many discrepancies still exist in the description of cervical fascia.

As stated by Dr. Levitt (1970):

The confusion in the descriptions of the cervical fascia is due to two major

reasons; (1) great difficulties exist in the anatomical dissection of the fascial

spaces since false spaces are created and true spaces are obliterated; (2) artificial

grouping and classification of spaces for descriptive purposes is difficult and

greatly reflects the emphasis of the author. Both pure anatomists and surgically

oriented clinicians have described these fasciae from their own points of view,

each one stressing what he feels to be important from the level of his particular

interests, thus creating different opinions on the subject. (p. 409)

In general, the fasciae of the head and neck are divided into two main layers: 1) superficial fascia, which is just deep to the skin and participates in the formation of the superficial spaces, and 2) deep fascia, which is more complicated and divided into three layers including: i) the superficial layer or investing fascia, ii) the middle layer or pretracheal fascia and iii) the deep layer or prevertebral fascia (Figure 3) (Lindner 1986;

Osborn et al., 2008).

The investing fascia is the principal layer of the deep cervical fascia, as the other layers of deep fascia emerge as septa from it (Lindner, 1986). The investing fascia is a well-defined fascial layer that surrounds the entire neck deep to the subcutaneous tissue.

Since the investing layer of the cervical fascia is thick, the spread of infection from the masticator space to the submandibular space is rare in patients whose infection mainly occupies the masticatory space. (Ariji et al., 2002)

Pretracheal fascia is derived from the investing layer and divided into a visceral and muscular part. The visceral part sheaths all the viscera that are located deep to the investing layer, including the pharynx, larynx, esophagus, trachea, thyroid, and parathyroid glands. The muscular part encloses the infrahyoid muscles, which are attached to the hyoid bone and thyroid cartilage, and extends from the hyoid bone superiorly to the superior mediastinum inferiorly where it blends with the fibrous pericardium. It continuous posteriorly and superiorly as the buccopharyngeal fascia,

which covers the constrictor muscles of the pharynx and the buccinator muscle, and blends laterally with the carotid sheath.

Prevertebral fascia is a part of the deep cervical fascia forms a tubular sheath and encloses the posterior half of the neck that lies deep to the trapezius muscle (Lindres,

1986). It encloses the vertebral column and the muscles associated with it, and extend from the base of the skull superiorly to the level of third thoracic vertebra, where it fuses with the anterior longitudinal ligament (Lindner, 1986; Moore & Agure, 2007).

Alar fascia is part of the vertebral fascia.It forms the anterior limit of danger space and posterior boundary of the retropharyngeal space. It extends from the base of the skull to the second thoracic vertebra where it fuses with the pretracheal fascia which forms the anterior wall of the retropharyngeal space.

1.4.2 Head and Neck Fascial Spaces:

An accurate diagnosis and treatment of head and neck infections needs a thorough knowledge of the fascia and different fascial spaces (Ariji et al., 2002). The fascial compartments that facilitate the spread of dental infections have been classified into superficial and deep spaces (Figures 1 & 3).

A. Superficial Spaces:

The superficial spaces include the vestibular, infraorbital, and buccal spaces. The superficial compartment lies between the skin and the upper and lower jaws covered by buccinator and masseter muscles. Cephalocaudally, it lies between the zygomatic bone 14

and the lower border of the mandible. The posterior boundary is the parotid fascia, which contains branches of the facial nerve, the muscles of facial expression, facial blood vessels, and buccal fat pads (de Vicente Rodriguez, 2004).

B. Deep Spaces

I. Spaces around the Mandible

The submental space is one of the deep facial spaces that lies deep to the investing layer of the deep cervical fascia. It is a potential space bounded laterally by the right and left anterior digastric muscles, superiorly by the mylohyoid muscles and platysma muscle inferiorly. Anteriorly is bounded by inferior border of mandible and posteriorly by the hyoid bone. It is posteriorly continuous with submandibular spaces.

The sublingual space lies between the mucous membrane of the floor of the mouth and the mylohyoid muscle. The mylohyoid muscle separates the sublingual space from the submandibular and submental spaces. Its medial boundaries are formed by the genioglossus, geniohyoid, and hyoglossus muscles. It contains the lingual and hypoglossal nerves, the sublingual glands, Wharton‘s duct, and the deep part of the submandibular gland. Anteriorly, the sublingual space on one side is continuous with the contralateral space, while posteriorly the space is continuous with the submandibular space around the posterior border of the mylohyoid muscle.

The submandibular space is the region between the mylohyoid muscle superiorly and superficial layer of the deep cervical fascia and the skin inferiorly and extends from 15

the mandible to the hyoid bone. The mylohyoid muscle separates the submandibular space from the sublingual space; both of them are continuous with each other at the posterior border of the muscle (Boscolo-Rizzo & Da Mosto, 2009). It is limited medially by the hyoglossus, posterior digastric, and stylohyoid muscles, laterally by the ramus of the mandible, and superficially by fascia and skin. It contains the superficial part of the submandibular gland, facial vessels, lingual nerve, and submandibular lymph nodes. The submandibular space communicates with the sublingual, submental, and the masticator space (de Vicente Rodriguez, 2004).

Submandibular infections usually arises from an odontogenic infection, especially from the second and third molar teeth, and exhibits severe signs and symptoms such as neck rigidity, trismus, swelling along the inferior border of the mandible, which may extend to the hyoid bone. Submandibular infections spread rapidly to the nearby spaces including the, parapharyngeal, retropharyngeal, or vesicular space, which may lead to airway obstruction (Ariji et al., 2002 & Ohshima et al., 2009).

The masticator space is formed by dividing the investing fascia at the inferior border of the mandible into two layers. The superficial layer covers the lateral side of the mandible and masseter muscle and fuses with superficial temporal fascia while the deep layer covers the medial pterygoid muscle continuously up to the base of the skull

(Schuknecht et al., 2008). It is composed of different inter-communicated spaces including: 1) the submasseteric space, which lies underneath the masseteric muscle, 2) 16

the pterygomandibular space, which is located between the medial aspect of the ramus of the mandible and the medial pterygoid muscle, and 3) the superficial and deep temporal spaces, which are related to the temporalis muscle and fascia. These subdivisions of the masticator space communicate with adjacent spaces, such as the submandibular, parapharyngeal (see below), and superficial spaces.

Infections of the masticator space commonly arise from the mandibular third molar tooth (Kim et al., 2011). The masseter and medial pterygoid muscles are usually involved with masticator space infections, which spread to the parapharyngeal space medially, the parotid space posteriorly, the buccal space anteriorly, and the mandibular space inferiorly (Yonetsu et al., 1998; Schuknecht et al., 2008). Masticator space infections are one of the manifestations of advanced odontogenic infections and if it involves the masticator muscles may lead to the development of trismus (Schuknecht et al., 2008). Trismus [limited mouth opening] is secondary to the muscle spasm and pain and is one of the important signs indicating the presence of a serious infection that may lead to a life-threatening situation (Dhanrajani & Jonaidel, 2002; Kaluskar et al., 2007).

II. Spaces Running through the Entire Length of the Neck

The spaces that run the entire length of the neck include the retropharyngeal space, danger space, prevertebral space, carotid space, parapharyngeal space and paratonsillar space. The retropharyngeal space (RPS) lies posterior to the pharynx and esophagus. It extends from the base of the skull to the level of the second thoracic 17

vertebra, where the fascial layers of the anterior and posterior walls join (Figure 4). The anterior wall is formed by the buccopharyngeal fascia superiorly and the visceral part of the middle layer of deep cervical fascia inferiorly. The posterior wall is made of the alar layer of the deep fascia, which extends from the base of the skull to the second thoracic vertebra. Laterally, the RPS is bounded by the carotid sheath (Elizabeth, 2002).

The Danger Space (DS) extends from the base of the skull to the diaphragm and lies between the alar layer anteriorly and the prevertebral layer posteriorly [Figure 4]. It is so named because its tissue offers less resistance to the infection, so the infection can easily spread to the posterior mediastinum of the thorax (Elizabeth, 2002).

The Prevertebral Space (PS) lies between the prevertebral fascia anteriorly and the vertebral column posteriorly, and the lateral boundary is made by the attachment of the fascia to the transverse process. The PS extends from the skull base to the coccyx and contains the anterior longitudinal ligament, longus colli muscles (Elizabeth, 2002; Lypka

& Hammoudeh, 2011).

The Carotid Space (CS) is the potential space that lies inside the carotid sheath. It is formed by contributions from the three layers of the deep cervical fascia (investing, pretracheal, and prevertebral layers). Infections in this space can spread to any of the deep neck spaces via direct paths (Rosen, 2002).

The Parapharyngeal Space (PPS) is the area lying lateral to the pharyngobasilar fascia. It has an inverted cone shape with a superior base at the base of the skull and the 18

apex at the level of the hyoid bone. Laterally, the PPS is bounded by the deep lobe of the parotid gland in the posterior direction and the medial pterygoid muscle with its covering fasciae anteriorly. Medially, the PPS is bounded by the constrictor muscles of the pharynx and the buccopharyngeal fasciae in the anterior part, and retropharyngeal space posteriorly. The PPS is limited posteriorly by the vertebrae and prevertebral muscles. The pterygomandibular raphe that gives attachment to the superior constrictor muscle and buccinators is the anterior limit for the PPS.

The styloid process and the musculofibrous tissues that are attached to it

(stylohyoid, stylopharyngeus, and styloglossus muscles and the stylohyoid and stylomandibular ligaments) divide the PPS into two separate compartments. The prestyloid or the anterior compartment is located anterior to the styloid process and contains lymph nodes and fat. The posterior or poststyloid compartment is located posterior to the styloid process and contains the cranial nerves IX through XII, the internal jugular vein, and the internal carotid artery. It has communication with other fascial spaces. Medially, it communicates with the retropharyngeal space, laterally with masticator space, and inferiorly with the submandibular space (Sichel et al., 2006; Lypka

& Hammoudeh, 2011).

The mediastinum is a middle septum of the thorax that is situated between the two pulmonary cavities; it contains vital organs such as the heart, great vessels, trachea, and esophagus. It extends from the superior boundaries of the thoracic to the diaphragm 19

inferiorly, and anteriorly from the sternum and anterior ends of the ribs to the bodies of

the thoracic vertebrae posteriorly. It divides into 4-compartments: superior and inferior

(anterior, middle, and posterior). The boundaries between these divisions are artificial

and not anatomical (meaning no fascial barriers) which makes the infections spread easily

between the compartment (Moore & Agure, 2007; Carter et al., 2014)

Danger Space

Retropharyngeal space

Figure 4. Shows the retropharyngeal space (blue) and danger space (red) (Image from Smith et al., 1999).

1.5 Maxillofacial Infections and Diagnosis

Successful treatment of maxillofacial infections depends on early diagnosis. The clinical diagnosis of deep head and neck infections is challenging but can still be made based upon the signs and symptoms that contribute to the severity of the infection, such as dehydration, dysphagia, respiratory distress, malaise, high fever, dyspnea, altered level of consciousness, and lymphadenopathy (de Vicente Rodriguez, 2004; Michael et al.,

2011).

Odontogenic infections tend to spread quickly through the various fascial planes, and lead to complications of different degrees. The degree of infections may vary from those that can be treated with antibiotics to those that require surgical intervention. Health professionals who treat patients with infectious diseases often face very serious challenges. The most important consideration with the deep neck infection is deciding the time and location of surgical intervention.

Smith et al., (2006) reported a positive relationship between CT image with contrast-enhanced material and outcome of the surgical findings. Several investigators have studied the deep neck infection with magnatic resonance imaging (MRI) and ultra sound (US) technique (Glasier et al., 1992; Matt & Lusk, 1987). MRI is not used in daily practice with the same frequency of the CT in diagnosis of deep neck infection because it

is more expensive, needs longer scanning time, and cannot give more information when compared with CT. Despite US technique proved its ability in identifying some conditions like the adenitis, it was not accurate in recognizing the deep abscess as well as the different anatomical landmarks (Smith et al., 2006).

1.6 Standard Clinical Imaging

Imaging is an important diagnostic tool for determining the source of the infection. Different types of radiographs are used in the diagnosis of different conditions in the maxillofacial region: intraoral periapical radiographs, bitewing radiographs, orthopantogram (OPG), and cephalograms. However, most of these images are taken to determine the origin of infection but have limitations because the information is rendered in two dimensions, within the limited area of view, and the different landmarks may not be well identified (Maeda et al., 2006).

One of the most important tools that can assist the clinical examination in early diagnosis of orofacial infections is CT scans (Salam, 2008; Lypka & Hammoudeh, 2011).

CT scans are superior to conventional imaging, because the superimposition of the anatomical structures that occurs on the conventional radiograph, can be eliminated by high contrast resolution, which allows for distinguishing between the different tissue contrast (Shahbazian & Jacobs, 2012)

When an infection occupies the deep fascial spaces, CT and magnetic resonance imaging (MRI) are used to localize the extent of infection by providing detailed 22

anatomical landmarks of the head and neck. CT is the modality of choice for studying and scanning maxillofacial infections. It is more available than MRI, less expensive, and more valuable in determining the progression from cellulitis to abscess formation [Hamza et al., 2003]. CT with contrast enhancement material is valuable in predicting deep head and neck infections, and many studies have demonstrated the ability of contrast-enhanced

CT in differentiating cellulitis from abscess (Lypka & Hammoudeh, 2011).

Thorough study and analysis of the radiological appearance and the user‘s ability to understand the anatomical landmarks of the region are important in localizing the lesion. In addition, the anatomical location and relationship to other structures are known to contribute to the potential severity of the infection and its degree of spread (El-Sayed

& Al-Dousary, 1996; Holt et al., 1982; Lazor et al., 1994). The usage of contrast material creates an enhancement ridge of tissues around the radiolucent areas with different degrees of thickness. The inflamed tissues, such as the muscles and glands, are enlarged and edematous, while the fat tissues are masked (Barton & Jane, 2003).

Recent advances in computer technology allow CT scans to produce 3D mapping of the anatomical and pathological detail of lesions. Consequently, the maxillofacial regions examined by 3D imaging are capable of revealing both the normal anatomy and pathology associated with orofacial infections (Cavalcanti et al., 2000).

1.7 Techniques for 3D Reconstruction

Many clinical applications have been studied by three-dimensional reconstruction

(Greess et al., 2000; El-Baz et al., 2011). For example, in the vertebral column, several lesions such as trauma, inflammatory, tumors, and degenerative conditions, have been studied. Three-dimensional reconstruction is optimal in showing the fracture line of the spine and its extension, particularly in sagittal view (Ring et al., 2013). 3D image reconstruction is better at evaluating complex maxillofacial trauma providing a better understanding of anatomical relationships not easy identified using conventional radiograph or 2D imaging. 3D images not only show the spatial relationships, but they also enhance our understanding of the complexities often times missed in multiple 2D axial CT images (DeMarino et al., 1986).

Aoyagi et al. (2015) have reported the benefit of 3D CT reconstruction images in the study of the morphological appearance of the mouse tongue muscles. The success of a surgical operation can depend upon skillful preoperative planning and the use of advanced computer technology and imaging modalities can contribute positively in determining different surgical approaches. The most common issues evaluated by 3D reconstruction are: complex fractures, fascial deformities, dental implants, and orthodontic treatments (Hu et al., 2011).

Cavalcanti et al.(2002) reported that most of the information present in CT images can be preserved by using volume rendering rather than surface rendering. The internal structures of the body are more obvious and accurate in volume renderings, which cannot be afforded by surface rendering. Moreover, study of small blood vessels and maxillofacial neoplasms by volume renderings have been shown to be more accurate in evaluating these structures (Hopper et al., 2000; Johnson et al., 1998).

1.8 Osirix (3D Open-Source Software)

New technologies can contribute significantly in the development and enhancement of the surgeon‘s ability to deal with complex operation procedures. To simplify the approach and account for complex anatomical structures, you need preoperative CT images that can be manipulated with software programs to produce quantifiable 3D structures. OsiriX is a 3D software program that can provide manipulation, navigation and visualization of contrast-enhanced CT images (Volonté et al., 2011). Osirix provides different rendering modes such as volume and surface renderings. Volume rendering is the simplest and fastest way to construct 3D models from 2D images. Additionally, this software program offers navigation through the body from the skin to bone by changing the window level and width.

Osirix 3D software is manufactured as an independent program that can operate in the Mac OS X operating system. This software application is designed to permit the operator to access a group of functions that include an image database, which can be 25

collected by downloading to a specific directory, retrieving images from PACS or manually copying them from an external network media. ―OsiriX is distributed freely as open-source software under the GNU licensing scheme at the following Web site: http://homepage.mac.com/rossetantoine/osirix ― (Rosset et al., 2004, p. 215).

1.9 Potential Novel Applications of 3D images

Several investigators have described the use of 3D CT images to assess different diseases and conditions in the maxillofacial region (Marsh & Vannier, 1983; Parisi et al.,

1989). Congenital malformation in the craniofacial region has been studied by 3D CT images. The 3D images allow the visualization of structures that are often obscured in superimposed two-dimensional displays. Additional information is provided in 3D CT images through the ability to view images from different aspects (Kishi et al., 1992). 3D reconstructed CT images were shown to be superior over conventional CT images in evaluating fracture extent in facial trauma (Reuben et al., 2005).

The 3D study and analysis of the CT images of submandibular space infections is a novel study and represents a new approach to obtain information on the complexity of maxillofacial infections. The goal of this project will examine if visualization of the lesion after volume rendering will provide new information that can lead to a better diagnosis, prognosis and treatment the clinical problem.

1.10 Research Questions

This research was guided by the following questions:

1. Do the clinical variables in conjunction with 3D mapping of a submandibular

lesion and its associated muscles predict clinical outcomes?

2. Can 3D mapping be used to describe odontogenic abscess morphology, and if so,

are there morphological characteristics that predict greater morbidity?

3. Do any of the following four hypotheses provide enough data that can help in the

diagnosis of orofacial infections as well as predict their potential for spreading,

length of the recovery period, and need for surgery?

1.11 Purpose of this Study

The conceptual framework of this strategy aimed to improve the recovery rate for submandibular space infections is to find a tool that can help in obtaining an accurate diagnosis and thereby minimize the surgical risk, improve the treatment outcome, and reduce the treatment cost. The purpose of this retrospective study is to reduce the recovery time of patients by analyzing the lesion‘s size, shape, location and surrounding muscles using 3D-CT reconstruction images, and to test these parameters by comparing the preoperative findings with the morbidity of patients and severity of infection.

1.12 Hypotheses

1.12.1 Hypothesis I

It is hypothesized that the evaluation of submandibular space lesion size

(volume) in relationships to patient morbidity by 3D mapping of CT images will contribute positively in determining recovery time. It is predicted that the study of lesion volume will correlate to differences in patient morbidity. 3D software will permit the calculation of lesion volume. Prior clinical studies have used 3D mapping to evaluate the volume of bone grafts, and shown that it positively correlates with a good surgical outcome (Arias-Irimia et al., 2012).

1.12.2 Hypothesis II

It is hypothesized that the study of the shape and margin characteristics of the submandibular space infection as determined by 3D modeling of the lesion will correlate with patient morbidity and provide significant information that can help in the evaluation of treatment. Several studies have shown that the shape and margin of a tumor can help in discrimination between benign and malignant diagnosis (Dong et al., 2008; El-Baz et al., 2011; Furuya et al., 1999; Kawata et al., 1997).

1.12.3 Hypothesis III

It is hypothesized that determination of the lesion size and its relationship to adjacent structures using 3D-CT image renderings will provide some predictive information that will help in the evaluation of treatment outcome. In mandibular teeth infections, the submandibular space is the most common space involved. Infections may then spread to the other adjacent spaces such as, the sublingual space, the masticator 28

space, the lateral pharyngeal space medially and parotid space posteriorly, and finally to the two target muscles: the medial pterygoid and the masseter (Ariji et al., 2002;

Boscolo-Rizzo & Da Mosto 2009; Yonetsu et al., 1998). This project will examine the spatial relationship of the submandibular space infection to the adjacent regions by using the 3D-CT technique. DeMarino et al. (1986) demonstrated the capability of 3D-CT in providing a better understanding of spatial relationships of complex maxillofacial trauma that that provided by plain x-ray films or 2D computed tomographic images.

1.12.4 Hypothesis IV It is hypothesized that an increase in the size of the muscles is an indication of the degree of infection and its possible spread to the adjacent spaces, and therefore segmentation of the muscles that are surrounding the submandibular space may correlate with patient morbidity. Many investigators have reported that changes in muscle size and fatty obliteration between the muscles represent significant manifestations of inflammatory changes (Obayashi et al., 2004). The muscles most commonly involved with submandibular infections are the masseter, medial pterygoid and lateral pterygoid muscles.

The fascia is known to act as an effective barrier to prevent the spread of infections but once the infection breaks down the fascial layer and reaches the muscle, the muscle can contribute in transferring the infection into neighboring tissues. Increased muscle thickness and disruption of fat tissue between the muscles is one of the 29

indications of the spread of infections (Obayashi et al., 2004; Ariji et al., 2002). We hypothesize that measuring the size of target muscles will correlate with patient morbidity and delay of recovery.

1.13 Aims of this Study

This project proposes to utilize 3D volume renderings of submandibular space infections and their associated muscles to study the location and extent of infection in an attempt to predict the best treatment and surgical outcomes. One of the goals is to identify predictors of second operation and hospital LOS.

Chapter 2: Research Design and Methods

In this chapter, I illustrate the research techniques that we used to study the 3D mapping of submandibular space infections and compare the outcomes with the clinical data that had been taken from the patients‘ charts. This initial procedural argument applies to study this type of maxillofacial space infections in order to evaluate the 3D outcomes in terms of shape, size, and location. The strategy of this project is to study two types of data: the qualitative and quantitative data.

This project, will examine a 3D reconstruction of each CT image of a maxillofacial space infection in an effort to glean additional diagnostic information regarding submandibular space infections.

2.1 Project Design

This imaging project was approved by the institutional review board (IRB) with the identities of the patients being anonymized. The proposed method is divided into two basic approaches in assessing whether the 3D reconstruction images can offer additional information that can help predict the outcome of the treatment: clinical data collection and 3D data collection, each part can be further subdivided into different subdivisions.

The following diagram illustrates the different variables that were collected from the patients chart and three-dimensional reformatted images (Figure 5). The clinical findings, were collected from the medical charts, were evaluated and compared with 3D-CT

images, which are made by reformatting and segmenting, manually (Table 1). The clinical data are stored in Electronic Health Record and Information Warehouse. We provided them with different diagnostic codes (ICD-9) that belong to the odontogenic infections of head and neck and different CPT codes that tag the office procedures, head and neck surgery, and radiology. After receiving the different codes that determine the diagnosis and type of surgery, the patients‘ records were sent to us through the deparment of biomedical informatics (BMI) Data Analyst. He sent to us the following records after de-identification: demographics, laboratory results, clinical reports that include the type of infection, etiology, time of admission, time of operation, time of discharge, and CT images.

Figure 5. Design process of study showing the different clinical and 3D variables.

Table 1. Sample of different variables that have been selected for this project (complete variables in Appendix A) Renal Pt_ID Age Gender Race Caucasian African_A Diabetes Hypertension Disease Cancer African- 3 30 1 American 0 1 0 0 0 0 4 49 1 Caucasian 1 0 0 0 0 0 5 50 0 Caucasian 1 0 0 0 0 0 African- 6 54 0 American 0 1 1 1 0 0 African - 8 33 0 American 0 1 0 0 0 0 9 52 1 Caucasian 1 0 0 1 0 0 10 45 1 Caucasian 1 0 0 0 0 0 12 27 0 Caucasian 1 0 0 0 0 0 13 49 0 Caucasian 1 0 0 0 0 0 14 35 1 Caucasian 1 0 1 1 1 0 34 African- 15 59 0 American 0 1 0 1 0 0 17 70 0 Caucasian 1 0 0 1 0 0 18 47 0 Caucasian 1 0 0 0 0 0 African - 19 42 1 American 0 1 0 0 0 0 African- 21 62 0 American 0 1 1 1 0 0 22 52 0 Caucasian 1 0 0 0 0 0 24 50 0 Caucasian 1 0 0 0 0 0 28 27 1 Caucasian 1 0 1 0 0 0

2.1.1 Clinical Variables Collection

The clinical variables recorded from the patient‘s report were age, sex, race, ethnicity, etiology, number of surgical interventions, past medical history (e.g., hypertension, diabetes, cancer, and renal insufficiency) and the time of admission, discharge, and operation. The complete data collection is shown on appendix A.

2.1.1.1 Participants

Patient records were obtained from the Department of Biomedical Informatics at

The Ohio State University Wexner Medical Center, Columbus, OH. The total number of patients examined was 103 individuals who suffer from submandibular infection with spreading to the adjacent spaces between November 2011 and September 2014. These cases have been selected according to the previous studies, whereas the number of samples varies between 30 to 80 participants. We predict this number of cases (103 patients) would be sufficient to show both clinical and statistical significance.

2.1.1.2 Demographic Variables

Many demographic factors have an important contribution in the study of human populations. The goal of including the demographic variables is to examine the relationships of age, sex, and race with the morbidity of fascial space infections and determine their relationship to our 3D findings. The ages of the patients included in the study ranged from 18 to 73 years. All of the participants included in this study were 35

clinically diagnosed with submandibular space infections with or without involvement of other fascial spaces.

2.1.1.3 Systemic Diseases

There are predisposing factors that can contribute to the severity of infection and a longer recovery process (Peters et al., 1996). These include diabetes, renal disorders, advanced stage malignancy, and immunodeficiency conditions.

2.1.1.4 Time-Frame Variables

We obtained the time of admission, time of operation, and time of discharge and calculated the time interval between each of them. Ahmad et al. (2013) have shown that hospital LOS is one of the key factors that contribute to an increase in hospital costs.

2.1.1.5 Etiology of Infections

Although there are many different ways that the submandibular space become infected, studies have shown that the third mandibular molar is usually the cause of a submandibular space infections (Flynn et al., 2006; Ylijoki et al., 2001). In this study, we only examined the infections that were due to dental caries or periodontal disease or trauma.

2.1.1.6 Anatomical Variables

The study of anatomic features can help to determine how an infection spreads as well as help identify important structures that may become involved in this process such 36

as the larynx, heart, lungs, carotid sheath and cavernous sinus. The anatomical facial spaces that are involved in the spreading of an infection include the buccal space, sublingual space, submental space, masticator space, parotid space, parapharyngeal space, carotid space, retropharyngeal space, and mediastinum. According to Flynn et al.

(2006), infection severity can be categorized into three graded levels based upon its proximity to the air way and other vital structures such as the heart (Table 2).

2.1.1.7 Characteristics of Variables (Abscess vs Cellulitis)

The infectious variables include different types of microorganism. An abscess is defined as a cavity with a purulent exudate fluid and necrotic tissues. Abcesses can be drained surgically. Abscess can be recognized on CT images by the presence of a fluid collection that is surrounded by a rim of enhancement. Cellulitis is defined as a diffuse inflammation of the soft tissues without formation of purulent cavities and it cannot be drained surgically. CT images display cellulitis as an area of attenuation without a surrounding rim of enhancement. An early-developed abscess or infected edematous tissues without necrotic materials, is called phlegmon, and the terms cellulitis and phlegmon are used interchangeable (de Vicente Rodríguez, 2004). For the purpose of this study, we will use the term cellulitis.

2.1.1.8 Consequencies of Infection

All patients in this study had been exposed to the surgical incision and drainage,

some of which lead to difficulties in breathing and subsequent endotracheal intubation.

Airway obstruction, trismus, repetition of surgical procedures, enlargement of the

muscles and glands that are adjacent to the source of infections are considered

complications of the submandibular infection.

Table 2. Severity scores for sever odontogenic infection. Severity Score Anatomic Space Severity score = 1 Vestibular (Low risk to airway or vital structures) Subperiosteal Space of the body of the mandible Infraorbital Buccal Severity score = 2 Submandibular (Moderate risk to airway or vital Submental structures) Sublingual Pterygomandibular Submasseteric Superficial temporal Deep temporal Severity score = 3 Lateral pharyngeal (High risk to airway or vital structures) Retropharyngeal Pretracheal Danger space (space 4) Mediastinum Intracranial infection Table from Flynn et al., 2006

2.1.2 Three-Dimensional Variables

The 3D-CT images of lesions were used for measurement of lesion extension, volume, and shape. In addition, the volumetric measurement of associated muscles was calculated and compared with the size of the muscles on the non-affected side.

Volumetric measurement can be achieved after segmentation of the region of interest

(ROI). Osirix software allows the images to be studied from axial, coronal, or sagittal views slice by slice. The thickness of each CT dataset varies between 3mm for the axial and 2mm for the coronal and sagittal images. We compared each of the above variables individually with the clinical findings, in order to find the specific volume, shape, and location that correlate with the worse course of infections.

2.1.2.1 Volume of the Infection

The volume of the infection, as a variable, has been used by many investigators to study other types of lesions and predict their outcome (Doweck et al., 2002; Xu et al.,

2008). According to Clauser et al. (2014), measuring the volume of breast cancer using different imaging techniques, including 3D ultrasound, was reliable and successful in improving patient evaluation and outcome. Doweck et al. (2002) demonstrated that studying the volume of advanced head and neck cancer can give an accurate prediction of the treatment outcome. Although unsupported by evidence, there is a commonly held sense among clinicians that a ―large‖ abscess is a ―worse‖ prognostic sign. 39

2.1.2.2 Shape of the Infection

Morphological study of lesion shape can be performed after volume rendering, and thereby be used to convert 2D image into a 3D image. Based on the study of pulmonary nodules (Iwano et al., 2005), lesions were classified into seven shapes including round, polygonal, tentacluar, irregular, speculated, ragged, and lobulated nodules. The authors found that speculated and ragged nodules appeared malignant and lobulated nodules were benign or malignant, with the remainder being benign. For our study, we hypothesize that lesion shape will correlate with the severeity of infection. The

3D lesions can be manipulated in space and examined from different aspects to determine which type of model correlates with a worse infection (Figures 6 & 7).

2.1.2.3 Spatial Relationship

3D mapping and modeling can help clarify the spatial relationships of a lesion much better than conventional imaging (Tatar, 2008). Studying the relationship between the ROI and the rest of the 3D region can be accomplished by first rendering the whole

3D image and then utilizing the ROI tool to show the lesion in its original position

(Figure 8). We can do the same procedures with the segmented muscles (Figure 9). In this way, we can review the location of each lesion and evaluate its relationship to the adjacent structures. 40

2.1.2.4 Fluid Density

Hounsfield unit (HU) measurements of different ROIs can be determined by the

3D software for all the lesion slices used in the formation of the 3D image. Measurement of the lesion density can be used to determine the percentage of fluid in each ROI.

2.1.2.5 Volume of Involved Muscles

The swelling of the muscles that surround the fascial space as well as fat obliteration are good indications that an infection is spreading (Yonetsu et al., 1998). The previous procedures can be carried out to build 3D model of the muscles that are associated with the infection in order to study their relationship with the lesion and compare their size with controls (Figure 9). We hypothesize that enlarged infected muscles can be used to predict the severity of infection.

2.2 Instruments

2.2.1 Computed Tomography (CT) Images All images are obtained using the helical technique after intravenous administration of a nonionic contrast-enhanced material (i.e., Omnipaque 50 milliliter) and scanned from the top of the aortic arch through the skull base. The images are stored in the warehouse of the department of medical maging informatica; the thicknesses of the images vary from 3mm in the axial plane to 2mm in the coronal and sagittal plains. CT images using radio-opaque material show a maxillofacial infection as a low-attenuation

area surrounded by enhancement rim of varying degrees of density. The lesion may appear lobulated, multifocal, or elongated.

2.2.2 OsiriX

For the purpose of maxillofacial pre-surgical planning, many software programs have been developed. The goal of our project is to navigate through large sets of multidimensional data using the open-source application called OsiriX that was freely downloaded from the Internet and can only be used on the Macintosh operating system.

The OsiriX program was first developed in 2004 by Rosset and Ratib, who were members of the Department of Medical Imaging and Information Science at the

University Hospital in Geneva, Switzerland (Melissano et al., 2009).

The OsiriX software program includes a database that is updated automatically when new images are downloaded to a specific directory. The images are either retrieved from PACS using a DICOM store function, or manually copied from the off-line media

(Rosset et al., 2004). Osirix has been used for research in the radiological and nuclear imaging fields (Bucsko 2007; Chen et al., 2008; Rosset et al., 2006). It has many tools that can be used for volume rendering, surface rendering, multiplanar rendering (MPR), and maximal intensity projection (MIP) (Wang et al., 2010).

Definitions of different modes:

Timothy Alberg (2010) giving the following definition for different modes:

3D Viewer Mode: 3D Viewer Mode allows the user to use multiple methods to view and align images on multiple planes. 3D MPR mode: The first option in this mode is 3D MPR. This opens up three windows, each one a different viewing plane of the series. 3D MIP mode: 3D MIP (Maximum Intensity Projection) is the first fully 3D mode under the 3D viewer. In this mode, the higher the attenuation, the brighter it shines. Specific details are difficult to make out in this mode; however, the shapes are still easy to distinguish. 3D Volume Rendering Tool: The 3D Volume Rendering Tool (VR for short) works exactly the same as the 3DMIP tool. Instead of projecting an intensity for specific volumes/attenuations, the VR 21tool displays the image using solid colors to represent volumes. 3D Surface Rendering: 3D Surface Rendering or 3D SR for short allows the user to create a 3D model based on a specific attenuation value. (pp. 8-24)

The OsiriX program offers different modes for rendering 3D images, with volume rendering being the fastest way to reconstruct 3D images and project them directly on a rendering of the patient‘s body surface to give the surgeon ability to see the exact position of the anatomical structures through the image (Sugimoto et al., 2009). In addition, this program has the ability to navigate through the patient‘s body from the skin to the deepest bone layer by changing the window level and width (Figures 14–17). The

time it takes for reconstructing a 3D ROI for evaluation can vary from a few hours to a whole day depending on the image and experence. After I have practiced and became familiar with the CT limited boundary of infection and associated muscles, it usually took only a few hours.

2.3 Procedures

Although different 3D techniques have been developed for remodeling of various conditions, none of them have been developed, for the 3D segmentation of submandibular space infections. In this study, we developed 3D reconstructed images for modeling of submandibular space infections in order to find a relationship between lesion parameters and the morbidity of the infection. Due to the similar intensity between the attenuated area of infection and the surrounding soft tissue, manual segmentation is more suitable and accurate than the computerized method. All the manual measurements were taken by a single operator.

2.3.1 Steps for ROI Segmentation with OsiriX Using a Manual Segmenting

Technique

To measure the volume of a lesion, I started from the beginning of its appearance in the CT slices and use the closed polygon tool, which is accessible from the ―ROI‖ pull down menu in the main menu bar. Highlight the lesion, and then click twice to close the polygon. I delineated by following the rim of enhancement that surrounds the low- 44

attenuation area. The low-attenuation areas possible were quadrangular, longitudinal, or lobulated with some irregularities (Figures 10 & 11). This procedure was repeated with each slice, or couple of slices, until the lesion disappears from the CT slices. Select ‗All

ROIs in the series‘ then give a name to all of the polygons. To render the volume, go to

ROI volume and then to ‗Generate Missing ROIs,‘ which then interpolates all of the missing slices between the first and last slices. The last step to build up and calculate the

3D volume of a lesion is to go to ROI volume and click ‗Compute Volume‘

In this study we reconstructed the lesions, as well as the muscles with the medial pterygoid colored green and the infection colored blue (Figure 9).

2.3.2 Steps for Muscle Segmentation with Osirix

Manual segmentation of the masseter muscle is more accurate than automatic segmentation, due to the unpredictability of the infection (Goto et al., 2002; Goto et al.,

2006). In this project, we used manual segmentation on the clear CT images of the muscles, followed by the interpolation of missing slices (Figure 12). The segmentation process was evaluated by a radiologist. Figure 13 illustrates the starting appearance of the masseter muscular tissue

2.3.3 Steps for Cropping 3D Volume with Osirix

This tool allows you to display the ROI that lies on the inner surface of the oral cavity by reducing the volume size of the 3D image.This allows optimal visualization of the lesion through all 3D views. Activating the cropping tool in the toolbar, leads to the appearance of a wire frame with green dots. By clicking and moving the frame we can adjust the 3D rendered images (Figure 9).

2.4 Methods of Analysis

We studied 103 submandibular lesions in this study. Statistical significance was set at a p-value ≤.05 where the chi square is used for the non-parametric data, paired- samples t-test and linear regression tests for parametric data, and independent sample t- test for both parametric and non-parametric. The different statistical analyses were used to determine the relationship between the selected clinical and 3D lesion variables. All of the data were statistically analyzed through the Statistical Package for Social Sciences

SPSS version 22 (SPSS Inc., Chicago, IL, USA).

Figure 6. The diagram illustrates the shape and volume of the left submandibular infection.

Figure 7. Other polygonal shape of the submandibular space infection. 47

Figure 8. Inferior oblique view of 3D-CT image shows 3D ROI of the submandibular infection (green) after relocated on its anatomical position.

Figure 9. Lingual aspect of the left 3D submandibular infection (dark blue) and medial pterygoid muscle (green) after cropping of the right side of the mandible.

Figure 10. Axial CT image demonstrates highlighting (blue line) of the ROI that represents the right submandibular space infection.

Figure 11. Highlighting (blue line) of two separate attenuation areas represented sublingual and submandibular infections.

Figure 12. 2D Image shows highlighting (green line) muscles on both affected and non-affected side.

Figure 13. Illustrates highlighting (blue line) the first slice of left masseter muscle and right submandibular infection.

Figure 14. 3D Volume rendering image shows the external appearance of the right submandibular infection.

Figure 15. Illustrates the relationship between the 3D submandibular space infection (green), the masseter, and the mylohyoid muscle.

Figure 16. Demonstrates different anatomical structures related to the polygonal shape of right submandibular infection (green).

Figure 17. Illustrates the spatial relationship of the polygonal shape of infection (green) to the mandible and blood vessels.

Chapter 3: Results

This chapter reports the results of the data collected from the 2D-CT images and from the 3D mapping of masticatory muscles and the submandibular infection space.

This study, I analyzed 103 cases of submandibular infections including other deep fascial spaces: such as the buccal, sublingual, submental, masticator, parapharyngeal, retropharyngeal, danger, carotid and mediastinal spaces. This retrospective study is divided into three parts: 1) clinical study, 2) three-dimensional study, which is considered a novel study, and 3) assessing the relationship of both clinical and three-dimensional variables to evaluate the following conditions: length of hospitalization (LOS) and a second operation.

3.1 Data Analysis

In this study, I defined second operation, and LOS as outcome, or dependent variables, and the clinical and three-dimensional variables as predictive, or independent variables. We computed the arithmetic mean, standard deviation, median, and mode for all the continuous variables. Based on the research hypothesis, aim of study, and collected data, these statistical tests have been selected: the chi-square test, sample paired

T-test, independent sample T-test, and linear regression. The collected variables were divided into categorical and continuous variables. When comparing two categorical

variables, a chi-square test is used; however, an independent sample T-test is used to compare a continuous variable to a categorical one. The paired sample t test is used to compare between the infected and non-infected muscles in an individual. In the case of comparing two continuous variables of different groups, a linear regression test is used.

3.2 Descriptive Statistics

3.2.1 Clinical Study

In this section, we report descriptive statistics by examining the total number and percentage of the patients for each variable including measurements of mean and standard deviation. All patients who are enrolled in this study presented with a submandibular space infection that had spread to other fascial spaces. A total of 103 patients were enrolled in the study composed of 52 males and 51 females. The mean patient age was (39.3± 12.6 years) with a range from 18 to 70 years. Figure 18 shows the relationship of different decades of life and the maxillofacial infections. The fourth and third decades in consecutive were the most common decades involved with odontogenic infections.

Patient ethnicity of the study group included 26.2% African American (n=27) and 73.7% Caucasians (n=76). These data are summarized in Table 3. Table 4 lists the debilitating conditions that enhance the primary comorbidities associated with our study group. Of our 103 enrolled patients, 11% (n=11) had diabetes, 18% (n =18) had 59

hypertension, 5% (n=5) had cancer and 3% (n=3) had renal disease. Table 5 lists the most common complications in our study group that were induced by the submandibular space infection. Additionally, 6.8% of subjects complained of trismus (n=7), 28.2% reported submandibular gland enlargement (n=29), 5.8% showed parotid gland enlargement (n=6), and 19.4% had airway obstruction (n=20).

Table 3. Demographic variables of the study group. Demographic Variables No. of Cases % of Cases Range Mean±SD Age (years) 103 100.0 18-70 39.3±12.6

Sex Male 52 50.5 Female 51 49.5

Race Caucasian 76 73.7 African-American 27 26.2

Table 4. Systemic diseases associated with the study group. Systemic Diseases No. of cases % of cases Hypertension 18 18%

Diabetes 11 11%

Cancer 5 5%

Renal Disease 3 3%

Figure 18. This diagram demonstrates the distribution of deep facial space infection among different decades.

Table 5. Consequences of submandibular infections. Consequences No. of Cases % of Cases Enlargement of Submandibular 29 28.2 Gland

Air-way Narrowing 20 19.4

Trismus 7 6.8

Enlargement of Parotid Gland 6 5.8

3.2.1.2 Time-Related Variables

Few studies have identified the factors that may explain the variation in length of hospital admission (Peters et al., 1996). The purpose of this study is to measure hospital

LOS and correlate it with other clinical and 3D variables. Table 6 shows the descriptive statistics on hospital LOS. The mean hours between admission and first surgery for our study group was 19.9±22.2 hours with a range from 0.0 – 150 hours, while the most frequent time between admission and first surgery was 9 hours. The mean hospital LOS for our study group was 5.2 days ± 3.0 days, with a range from 1.3 to 16.0 and the LOS mode was 1.3 days. I was excluded, however, one outlier whose hospital LOS was over

45 days, due to significant comorbidities that included hypertension and cancer in addition to a submandibular space infection.

The mean and standard deviation of the interval period between the admission and second surgery was 101.9, ±65.5 hours with a range from 17.4 – 208.9 hours. The median time between admission and second surgery was 91.2 hours and the mode was 17.4 hours. Out of 103 cases, 11% of the patients underwent a second operation after the first surgery. The patients had an average stay of 8.0 ± 3.2 days following their second operation with a range from 2 to 11 days. The median hospital LOS in this patient population was 7.0 days with the most frequent hospital LOS being, 2.2 days Table 7.

Table 6. Time interval between admission, time of first surgery, and discharge.

Time Between Adm. and First Name of Variable First LOS (Days) Surg. (Hours)

No. of cases 103 103

Mean± SD 19.9±22.2 5.2±3.0

Median 14.1 4.0

Range 0.0 – 150 1.3 – 16.0

Mode 9 1.3

Table 7. Time interval between admission, time of second surgery, and discharge.

Time Between. Adm. and Second Second LOS Name of Variable Surg. (Hours) (Days)

No. of cases 11 11

Mean± SD 101.9±65.5 8.0±3.2

Median 91.2 7.0

Range 17.4 – 208.9 2.2 – 12.0

Mode 17.4 2.2

3.2.1.3 Anatomical Location

Table 8 summarizes the anatomic location and extension of infections into the different fascial and neck spaces. The distribution of infections is divided into single and multiple space infections. The submandibular space is included in both single and multiple space infections. Single space infections were found in 9.7% of patients (10) while multiple space infections were found in 90.3% of patients (93).

With regard to other fascial space infections, many of the patients had multiple different types involved simultaneously, which is why the percentages go over a hundred. 50.5% of patients experienced sublingual space infection, 44.7% masticator space, 43.7% 64

submental space, 32.0% parapharyngeal space, 17.5% buccal space, and 8.7% retromolar region. Moreover, 8.7% of the patients experienced infections in the carotid space, 7.8% parotid space, 5.8% retropharyngeal space, 1.0% danger space, and 6.8% mediastinum space.

The sublingual space, which is separated from the submandibular space by the mylohyoid muscle and continues with it at the posterior border, is found to be the second most infected space after the submandibular space. The danger space, located between the retropharyngeal and prevertebral spaces, is found to be the least infected space- appearing in only 1% of the patients. The masticator space that surrounds the posterior boarder of the mandible was the third most infected space occurring in 44.7% of patients.

The submental space was found to be frequently involved in the spread of submandibular infection, occurring in 43.7% of patients. Other spaces were involved much less frequently, the parapharyngeal space, which is separated from the masticator space by the medial pterygoid muscle, was found to be involved in 32% of the patients, while infections in the buccal space occurred to 17.5% of the patients.

Table 9 shows 61 of 103 patients (59.2%) had an abscess, and 42 of 103 cases (40.8%) had cellulitis. Also, CT images of 34 patients demonstrated distribution of bacterial gases in different degrees (33.0%). The table shows side predilection in the distribution of infections: 49 (16.3%) were found on the right side and 54 (18.0 %) on the left. 65

Table 8. Anatomical distribution of infections. Facial Spaces No. of cases % of cases Multiple Space Infections 93 90.3

Sublingual Space 52 50.5

Masticator Space 46 44.7

Submental Space 45 43.7

Parapharyngeal Space 33 32.0

Buccal Space 18 17.5

Single Submandibular 10 9.7 Infection

Retromolar-Trigone Region 9.0 8.7

Carotid Space 9.0 8.7

Parotid Space 8.0 7.8

Mediastinum 7 6.8

Retropharyngeal Space 6 5.8

Danger Space 1 1.0

Table 9. Character of infections. Name of No. of Cases % of Cases Variable Abscess 61 59.2

Side Involved Left 54 52.0 Right 49 48.0

Cellulitis 42 40.8

Bacterial Gas 34 33.0

3.2.2 Three dimensional study

3.2.2.1 Measurements of 3D Associated Muscles

Table 10 shows the volume variation between both infected and non-infected muscles. The total volume for the 3D involved masseter (3DIM) is 2810.86 cm3, ranges from 8.5 to 61.5 cm3, mean: 27.3± 9.7 cm3, median: 25.5 cm3. The total volume for 3D masseter (3DM) is 2148.23 cm3, ranges from 8.4 to 43.0 cm3, mean: 20.9 ± 7.0 cm3, median: 19.5 cm3. The calculations for the 3D medial pterygoid (3DMP) are: total volume = 990.56 cm3, mean: 9.6±3.2 cm3, range from 4.6 to 20.4 cm3, and median 9.0

cm3. The 3D infected medial ptergoid (3DIMP) has the following measurements: total volume: 1335.32 cm3; mean: 13.4± 3.2 cm3, range from 3.0 to 40.9 cm3, and median:

11.9 cm3. The parameters of the 3D lateral pterygoid (3DLP) are: total volume: 667.00 cm3; mean: 6.5± 2.1 cm3; range from 3.1 to 11.2 cm3, and the median: 6.4 cm3, and the measurements of the infected 3D infected lateral pterygoid (3DILP) are: total volume =

679.97 cm3; mean: 6.6±2.0 cm3; range from 2.0-13.4 cm3, and a median: 6.5 cm3.

3.2.2.2 Measurements of Region of Interest (ROI)

I performed segmentation of the submandibular space infection in 83 CT images.

The measurements of the lesion are: total volume of all lesions = 838.34 cm3, mean =

10.6 ± 9.8 cm3, and the range of the individual size varies from 0.52 to 48.7 cm3.

Further, we also evaluated the fluid density of the different lesions: the mean = 0.123±

0.131 cm3, ranges from 0.001 to 0.652 cm3, the maximum fluid density of individual ROI is 65%, the total percentage of the fluid density for all lesions is 9.74% HU. All these data are summarized in Table 11.

3.2.2.3 Comparison between 3D Volumes of Infected and Non-Infected Muscles

Hypothesis:

H0: There is no difference in 3D volumes between the affected and non-affected masticatory muscles.

H1: There is a difference in 3D volumes between the affected and non-affected masticatory muscles. 68

Table 12, shows the different scores between the means and standard deviation of the infected and the healthy muscles, in particular the masseter and medial pterygoid muscles. The difference in the averages between 3DIM and 3DM = 6.4 cm3; the difference in the averages between the 3DIMP and 3DMP = 3.3 cm3, and lastly the difference in the averages between the 3DILP and 3DlP= 0.12 cm3.

The sample paired t-test found significant difference between the segmented 3D muscles; first pair: 3DIM - 3DM, t (102) = 7.300, p=0.001; the second pair: 3DIMP –

3DMP, t (102) = 6.390, p=0.001; however, no statistical significance was shown between the 3D involved lateral pterygoid and normal lateral pterygoid muscles (3DILP – 3DLP), t (102) =0.374, p=0.374. Therefore, the difference in size is only significant between the infected and non-infected masseter and medial pterygoid muscles. The results of the sample paired t test indicated that the differences in volume between muscles can be used as a predictive variable for treatment intervention of submandibular space infections.

Table 10. Measurements of affected and non-affected muscles. Medial Pterygoid Lateral Pterygoid Masseter Muscle Muscle Muscle Volume cm3 3DM 3DIM 3DMP 3DIMP 3DLP 3DILP

Mean± SD 20.9± 7.0 27.3± 9.7 9.6± 3.2 13.4± 3.2 6.5± 2.1 6.6± 2.0 (cm3)

Median (cm3) 19.5 25.5 9.0 11.9 6.4 6.5

Range (cm3) 8.4-43.0 8.5-61.5 4.6-20.4 3.0-40.9 3.1-11.2 2.0-13.4

Sum (cm3) 2148.23 2810.86 990.56 1335.32 667.00 679.97

3DM= Three dimensional of masseter; 3DIM=Three dimensional of infected masseter; 3DMP= Three dimensional of medial pterygoid; 3DIMP=Three dimensional of infected medial pterygoid; 3DLP= Three dimensional of lateral pterygoid; 3DILP= Three dimensional infected lateral pterygoid

Table 11. Measurement of ROIs volume and Fluid density. Volume ROI Volume cm3 ROI Fluid Density % N 83 83

0.123 Mean± SD 10.6± 9.8 ± 0.131

Range 0.521-48.742 0.002-0.652

Sum 838.347 9.748

ROI=Region of interest; Fluid density: number of Hounsfield units (HU)

Table 12. Three dimensional affected muscles vs three dimensional non- affected muscles. 3Dmuscle volume Mean± SD t p 3DIM 27.2±9.7 7.300 .001 Pair 1 3DM 20.8±7.0

3DIMP 12.9±6.0 6.390 .001 Pair 2 3DMP 9.6±3.2

3DILP 6.6±2.0 .892 .374 Pair 3 3DLP 6.4±1.7

Note: 3DIM= Three-dimensional infected masseter; 3DM= three-dimensional unaffected masseter; 3DIMP= Three-dimensional infected medial pterygoid; 3DMP= Three-dimensional unaffected medial pterygoid; 3DILP= Three-dimensional infected lateral pterygoid; 3DLP= Three-dimensional unaffected lateral pterygoid.

Table 13. Hypothesis results. H : There is no difference H : There is a difference 3D Muscles 0 1 between volume of 3D in size between 3D Volume infected muscles and 3D infected muscles and 3D Comparisons non-infected muscles. non-infected muscles. 3DIM vs 3DM Reject Accept

3DIMP vs 3DMP Reject Accept

3DILP vs 3DLP Accept Reject

3.3 Analytic Statistics

In this chapter, I need to evaluate the relationships between the predictive and independent variables or outcome variables, which include second surgery, and hospital

LOS. I examined the correlation between the predictive variables and outcome variables of our study. The Chi-Square Test was used to find any significant relationship between the dependent and independent nominal variables. The Paired Samples T-Test was used to compare between the means of the different volumes of 3D muscles of the affected and non-affected side of the face for the same individual. The Independent Sample T- Test was used to evaluate between the dependent categorical variables and independent continuous variable. Linear regression was used to correlate between the dependent and independent continuous variables. A p-value equal to or less than 0.05 (p≤.05) was

chosen as a cut-off for statistical significance in the study. As it is the standard for clinical study.

3.3.1 Second Surgery (SS)

In Table 14, the two variables did not show any significant association with a second operation. The gender and race variables have p values of 0.322 and 0.418 and therefore cannot be used to predict the occurrence of a second operation. Further, there was no statistical significance between a second operation and age (Chi-Square = -0.76, p= 0.444).

Chi-square test was also used to assess the relationship between systemic diseases and the repeating of surgical procedures. Our results found no evidence to link systemic diseases to a second operation (p value was greater than 0.05). Diabetic patients had a p

= 0.394; hypertensive patients had a p = 0.425; patients with renal disease had a p =

0.197; cancer patients had a p = 0.428 Table 15. The spss output data are found in

Appendix D.

In Table 16, the chi-square test of the facial spaces, which are adjacent to the mandible, shows no significant relationship to a second surgery, except for the retromolar region and submental spaces, which had p values < .05 (p= 0.021, p= 0.040), respectively. Chi-square results also revealed no statistically significant relationships 73

between the facial spaces that run along the entire length of the neck and a second operation. Based upon the data in Table 17, we are not confident that infected neck facial spaces correlate with a need for a second operation. Pearson correlation illustrates a weak connection between deep neck facial spaces and the occurrence of a second operation.

Therefore, the alternative hypothesis is rejected.

All the Pearson‘s correlations (R-value) are less than 0.30, therefore showing a weak relationship between the independent and explanatory variables. Results of the Chi- square test (p-value) show no statistically significant relationship between the variables that are mentioned in Table 18 in predicting a second surgical procedure.

3.3.1.2 3D Variables vs Second Surgery

Hypothesis:

H0: The 3D muscle volumes and ROIs cannot act as predictors for a second surgery.

H1: The 3D muscle volumes and ROIs can act as predictors for a second surgery.

In Table 19, no significant relationship was found between the four variables of the 3D study and a second surgery. The 3DIM, t (101) = 0.76, p= 0.444; the 3DIMP, t

(101) = 0.04, p= 0.962; the 3DILP, t (101) = 0.89, p= 0.373; and finally the total ROIs, t

(77) = 0.60, p= 0.550. In the third column, there wasn‘t a major difference in the means and standard deviations between cases that were exposed and not exposed to a second 74

operation. Therefore, the 3DIM, 3DIMP, 3DILP, and total ROIs variables were not statistically significant predictors for repetition of surgery.

Table 14. Relationships between gender, race, and second operation. Second Operation Variable Name % of Affected Chi-Square df p Patients Gender 0.98 1 0.322 Female 14% (7 of 51) Male 8% (4 of 52)

Race 0.65 1 0.418 African-American 15% (4 of 27)

Caucasian 9% (7 of 76)

Age 11% (11 of 103) -0.76 101 0.444

Table 15. Relationships between the independent variables and dependent variable second surgery. Second Operation Variable Name % of Affected Chi-Square df p Patients Renal disease 33% (1 of 3) 1.66 1 0.197

Diabetes 18% (2 of 11) 0.72 1 0.394

Hypertension 16% (3 of 19) 0.63 1 0.425

Cancer 0% ( 0 of 5) 0.62 1 0.428

Table 16. Relationship between facial spaces around the mandible and the second operation. Second Operation Variable Name % of Patients Chi-Square df p Exposed to SO Retromolar Trigon Region 33% (3 of 9) 5.30 1 0.021

Submental Space 18% (8 of 45) 4.22 1 0.040

Sublingual Space 15% (8 of 52) 2.43 1 0.118

Parotid Space 25% (2 of 8) 1.86 1 0.172

Masticator Space 15% (7 of 46) 1.77 1 0.180

Buccal Space 17% (3 of 18) 0.82 1 0.365

Table 17. Relationships of facial spaces along the entire length of the neck with the Second Surgery. Second operation Variable Name % of patients Chi-Square df p exposed to SO Multiple space infection 12% (11 of 93) 1.324 1 0.250

Mediastinum 0% (0 of 7) .898 1 0.343

Retropharyngeal space 0% (0 of 6) .762 1 0.383

Danger Space 0% (0 of 1) .121 1 0.728

Parapharyngeal Space 12% (4 of 33) .106 1 0.745

Carotid Space 11% (1 of 9) .002 1 0.965

Table 18. Relationship between infection's characters and second surgery. Second Operation Variable Name % of patients Chi-Square df p exposed to SO Enlarged submandibular gland 17% (5 of 29) 1.822 1 0.177

Trismus 0%( 0 of 7) 0.898 1 0.343

Enlarged parotid gland 17% (1 of 6) 0.239 1 0.625

Abscess 11% (7 of 61) 0.099 1 0.753

Bacterial gas 12% (4 of 34) 0.063 1 0.802

Airway narrowing 18% (3 of 17) 0.486 1 0.362

Table 19. Relationship between 3D variables and second surgery. Second Operation Variable Name % of Patients Chi-Square df p Exposed to SO Mean±SD 3DILP% 11% (11 of 103) 166.1±85.9 0.888 101 0.377

3DIM% 11% (11 of 103) 158.5±76.4 -1.218 101 0.226

3DIMP% 11% (11 of 103) 166.1±85.9 -1.398 101 0.165

ROI volume 14% (11 of 79) 8.9±7.0 0.600 77 0.377

Table 20. Hypothesis results in predictions of second surgery. H0: There is no difference in H1: There is a difference prediction of second surgery in prediction of second between the different surgery between the variables different variables Demographic variables Gender Accept Reject Race Accept Reject Age Accept Reject Debilitating conditions Diabetes Accept Reject Hypertension Accept Reject Renal Disease Accept Reject Cancer Accept Reject Spaces around the mandible Buccal Space Accept Reject Retromolar Region Reject Accept Submental Space Reject Accept Sublingual Space Accept Reject Parotid Space Accept Reject Masticator Space Accept Reject Spaces Along the Entire Length of the Neck Parapharyngeal Space Accept Reject Carotid Space Accept Reject Retropharyngeal Space Accept Reject Danger Space Accept Reject Mediastinum Accept Reject Character of Infection and Complications Abscess Accept Reject Trismus Accept Reject Bacterial Gas Accept Reject Enlargement of Accept Reject Submandibular Gland Enlargement of Parotid Accept Reject Gland Continued 79

Table 20. (Continued) Airway narrowing Accept Reject Three Dimensional Variables (3D) 3DIM Accept Reject 3DIMP Accept Reject 3DILP Accept Reject ROIs Accept Reject

3.3.2 Hospital LOS

The gender group was divided into females (N=51) and males (N= 52) with mean and standard deviation of 5.7±6.3; and 5.4±3.2, respectively. To test the hypothesis that gender is linked to LOS, an independent samples t-test was performed. As can be seen in

Table 21, LOS is distributed evenly amongst both genders (t (101) = 0.32, p = 0.748).

Therefore, gender is not a factor that is correlated with LOS. In addition, the assumption of variances was examined and satisfied through Levene‘s F test, F =1.31, p = 0.254.

Similarly, race didn‘t show any significant relationship to LOS. The assumption of variance was examined and assumed equal via Levene‘s F test, F (101) = 0.64, p =

0.423. African-Americans patients (n =27) had an average LOS of 5.2 days (±2.9).

Caucasian patients (n = 76) had an average LOS of 5.7 days (± 5.5). Therefore, race doesn‘t impact LOS. The independent t-test shows that t (101) = -0.46, p = 0.645.

Cohen‘s d was estimated at 0.09, which further proves that race and LOS aren‘t linked.

Table 22 shows the results of an independent samples t-test that was conducted to compare debilitating conditions to LOS. None of the debilitating conditions were detrimental to LOS. Diabetes: t (101) = -1.30, p= 0.195, (M±SD = 7.4±4.1), the assumption of variance F (101) = 0.84, p = .360. Hypertension: t (18.6) = -1.34 p = 0.195, assumption of variance was F (101) = 16.95, p = 0.000. Renal disease: t (101) =-1.18, p =

0.238, the leven‘s test was satisfied F (101) =0.29, p = 0.586. Cancer, t (4.0) = -0.80, p =

0.466, Leven‘s F test was significance F (101) = 64.694, p = 0.000.

Table 21. Relationships between Gender, Race, and LOS. LOS Variable Name % of Affected Mean ± SD Chi-Square df p Patients (days)

Gender .323 101 .748 Female 52 5.4±3.2 Male 51 5.7±6.3

Race -.462 101 .645 Caucasian 76 5.7±5.5

African-American 27 5.2±2.9

Age 103 39.3±12.6 2.188 101 .031

Table 22. Relationships between systemic disease, and LOS. LOS Variable Name No. of patients Mean ± SD t df p Diabetes 11 7.4±4.1 -1.30 101 0.195

Hypertension 19 8.0±9.8 -1.34 18.6 0.195

Renal disease 3 8.9±5.1 -1.18 101 0.238

Cancer 5 12.0±18.6 -0.80 4.0 0.466

An independent sample t-test showed that there is no statistically significant relationship between the facial spaces around the mandible and LOS. In Table 23, all of the following spaces had a p value > 0.05: buccal space, t(101) = -0.03, p = 0.970; retromolar region(101) = -1.18, p = 0.240; sublingual space, t(101) = -0.20, p = 0.842; parotid space t(101) = -0.91, p = 0.363; and masticator space, t(101) = 0.17, p = 0.867.

The only space that showed a significant link to LOS was the submental space t (52.8) = -

2.605, p = 0.022. Therefore, the null hypothesis is accepted for all but the submental space.

As seen in Table 24, the independent samples t-test was conducted to compare the different facial spaces that run along the entire neck to LOS. The independent variables were parapharyngeal, carotid, retropharyngeal, danger, and mediastinal spaces.

The dependent variable is LOS. There was no significant difference between the

predictive and outcome variables. The following spaces have a higher p value than 0.05: carotid space (8.2±5.3), t(101) = -1.67, p =- 1.672; parapharyngeal space (5.8± 2.9), t(101) = -0.21, p = 0.830; retropharyngeal space (7.9±5.0), t(101) =-1.16, p = 0.248; danger space (11.8±0.0), t(101) = -1.25, p = 0.212; and the mediastinum (8.1±5.2),

Table 23. Relationship between the spaces located around the mandible and hospital LOS. Hospital LOS Variable Name No of Mean ± SD t df p Patients Sublingual Space 52 5.7±3.3 -.200 101 0.842

Masticator Space 46 5.5±3.4 .168 101 0.867

Submental Space 45 7.0±6.8 -2.605 52.8 0.022

Buccal Space 18 5.6±3.3 -.038 101 0.970

Retromolar Trigon 9 7.5±3.5 -1.181 101 0.240 Region

Parotid Space 8 7.1±3.2 -.914 101 0.363

Table 24. Relationship between the spaces running along the entire length of the neck and hospital LOS. Variable Name Hospital LOS No of Mean ± SD t df p patients Multiple Space 93 5.7±5.1 -1.210 101 0.229 Infection

Parapharyngeal Space 33 5.8±2.9 -.21 101 0.830

Carotid Space 9 8.2±5.3 -1.67 101 0.098

Mediastinum 7 8.1±5.2 -1.38 101 0.170

Retropharyngeal 6 7.9±5.0 -1.16 101 0.248 Space

Danger Space 1 11.8±0.0 -1.25 101 0.212

Table 25. Relationships between infection's characters and hospital LOS. Hospital LOS Variable Name No. of Patients Mean ±SD t df p

Abscess 61 5.8±3.4 -.572 101 0.569

Bacterial Gas 34 5.9±3.3 -.431 101 0.667

Enlargement of Submandibular 29 5.3±2.9 .300 101 0.764 Gland

Air way Narrowing 20 9.3±9.1 -2.270 19.7 0.035

Trismus 7 8.0±5.1 -1.337 101 0.184

Enlargement of Parotid Gland 6 13.8±15.8 -1.357 5.0 0.233

3.3.2.1 Hospital LOS vs 3D Variables

Hypothesis:

H0: The 3D muscle volumes and ROIs cannot act as predictors for the hospital LOS.

H1: The 3D muscle volumes and ROIs can act as predictors for the hospital LOS.

Table 33 shows the relationship of the confounding variables together with LOS.

The correlations between the 3D infected masseter and hospital LOS has a p = 0.122 and is not statistically significant. On the other hand, the correlation between the 3D infected medial pterygoid and hospital LOS has a p = 0.006, making it a significant correlation.

The association of the 3DILP with the hospital LOS has a p = 0.003, which means there is a statistically significant relationship between the two. The ROIs (p = 0.125) indicate there is an insignificant relationship with the hospital LOS.

In the first column, the correlations coefficient (R) of all 3D variables presents a strong relationship between the independent variables (3D) and the dependent variable (hospital

LOS) - R = 40.2%. This shows that combining 3D variables is an advantageous course for predicating the hospital LOS. The coefficient of determination (R square) is 0.117; therefore, about 11.7% of variation in the hospital LOS is explained by the volume of

ROIs and the 3D associated muscle volumes. Also, the p-value, p = 0.006, and p = 0.003 indicates that the model is statistically significant to predict hospital LOS. (See the appendix G) 86

Table 26. Relationship between 3D independent variables and LOS dependent variable (linear regression test ). Hospital LOS Model Adjusted R R t p square 3DIM% .058 -.006 -.584 0.560 3DIMP% .042 -.008 .427 0.670 3DILP% .179 .022 -1.828 0.070 ROIs .128 .004 1.131 0.262

Table 27. Hypothesis results in predictions of LOS. H0: There is no difference H1: There is a difference in prediction of Hospital in prediction of Hospital LOS exposing between LOS exposing between the different variables the different variables Demographic Variables Gender Accept Reject Race Accept Reject Age Reject Accept Debilitating Conditions Diabetes Accept Reject Hypertension Accept Reject Renal disease Accept Reject Cancer Accept Reject Spaces Around the Mandible Buccal Space Accept Reject Retromolar Region Accept Reject Submental Space Reject Accept Sublingual Space Accept Reject Parotid Space Accept Reject Masticator Space Accept Reject Continued

Table 27. (Continued) Spaces Along the Entire Length of the Neck Parapharyngeal Space Accept Reject Carotid Space Accept Reject Retropharyngeal Space Accept Reject Danger Space Accept Reject Mediastinum Accept Reject Character of Infection and Complications Abscess Accept Reject Trismus Accept Reject Bacterial Gas Accept Reject Enlargement of Accept Reject Submandibular Gland Enlargement of Parotid Accept Reject Gland Narrowing of Air way Reject Accept Three Dimensional (3D)Variables 3DIM Accept Reject 3DIMP Accept Reject 3DILP Accept Reject ROI Volume Accept Reject

3.4 3D-CT Spatial Relationship of Submandibular Space Infections and Adjacent Structures

Hypothesis:

H0: The 3D study of spatial relationships of a submandibular space infection cannot provide any information that can help predict surgical outcome.

H1: The 3D study of spatial relationships of a submandibular infection can provide information that can help predict surgical outcome.

For a successful submandibular space surgical procedure, accurate preoperative planning and examination is necessary. The maxillofacial region has very complicated structures that are divided by fascia into different facial spaces. This leads to difficulty in understanding the spatial relationships between the submandibular space and other adjacent structures. A submandibular space infection is segmented from the CT data by using the closed polygon tool. The 3D shape of the lesion is reconstructed as a polygonal structure and can be manipulated in 3D permitting the spatial relationships to be more visible.

Figure 19 shows 3D image of a masticator space infection in relationship to the adjacent masticator muscles and mandible. From this image, we can understand spatial relationships, space infections, and other maxillofacial structures from multiple views. In this case, the medial pterygoid and masseter muscles surrounding the infection can become indexes that show the spatial location of the abnormal tissue. Based on the 3D images, the masticator space appears to be the primary site of infection (submasseteric & pterygo-mandibular space), while the submandibular space was involved secondary to the masticator space.

The patient in Figure 20 suffered from swelling related to the right side of the face. 3D-CT images allow the operator to view the lesion from various aspects to help determine the extension of infection. By analyzing this figure, you can localize a lesion to 89

a particular space. The 3D image can demonstrate the extension of the submandibular space infection anteriorly toward the submental space along the inferior border of the mandible. After changing the contrast of the image, the window displays the soft tissues: mylohyoid muscle, geniohyoid muscle, and external carotid artery. Appearance of soft tissues adjacent to the 3D infection can help in better understanding the anatomical relationships of these structures. The submandibular space is superiorly bounded by mylohyoid muscle, inferiorly by the superficial layer of deep cervical fascia, and extends from the inferior border of mandible to the hyoid bone.

Figure 21 shows a submandibular infection related to the right side of the mandible. The different views show the lesion from different aspects. Panel (a) shows the extension of cuboidal model that represents the infection below the inferior border of mandible and masseter muscle (green). Panel (b) illustrates the lesion from the lingual side and its relationship to the inferior alveolar canal and oblique ridge, which can be used as indexes to localize the infection. Panel (c) shows the lesion from the lateral oblique view and its relationship to the adjacent soft tissue. The last panel (d) shows the relationship between the 3D reformatted medial muscle, masseter, and lateral pterygoid muscles (red) with the submandibular infection from the posterior aspect..

Figure 22 (a) shows a swelling related to the lower right side of the face. Panel (b) shows the 3D reformatted shapes of the masseter muscle (green) and submandibular 90

infection, which extends to the right buccal region and cross the midline to the left side of submental space, indicating a Ludwig‘s angina infection. Panel (c) shows the 3D modeling of the infection, masseter, and medial pterygoid muscles in relationship to posterior border of the ramus of the mandible and the branches of common carotid artery.

In panel (d), the 3D reformatted infection and medial pterygoid can be viewed from the lingual aspect, after cropping the left side of mandible. The close relationship between the lesion and the hyoid bone can give indication that the infection may spread to parapharyngeal space and retropharyngeal space.

Figure 23 shows the spatial relationship of a submandibular infection to the adjacent soft tissues. In panel (a) we can see the relationship between the 3D modeling of infection and anterior belly of digastric muscle and mylohyoid muscle. Panel (b) shows the relationship of the masseter muscle and submandibular gland with the 3D submandibular space infection. The last panel (c) shows the swelling of the right masseter muscle and the right submandibular gland.

Figure 19. 3D polygon of left masticator space infection and associated muscles (masseter & medial pterygoid). (35 years male, Caucasian, LOS: 11.7). 3D-CT reconstructed imaging show swelling related to the leftside of the face (a), demonstrating the relationship of the submandibular infection (blue) and masticatory muscles (green) (b), inferior view shows the extension of infection (blue) to the buccal and lingual sides of masticator space (c), posterior view shows the relationship of infection (blue) to the ramus of mandible and styloid process. 92

Figure 20. 3D-CT images show the relationships of polygonal shape of right submandibular infection to the adjacent structures. (Male patient, 51 y, Caucasian, LOS: 14.7). 3D-CT imaging shows swelling related to the right submandibular region (a), lateral-oblique view demonstrates the relationship of 3D reconstructed infection (blue) to the masseter and medial pterygoid muscles (green) (b), lateral view shows the extension of infection (blue) along the inferior border of the mandible and its relationships to the external carotid artery (c), inferior view display the inferior aspect of infection (blue) that involved the submandibular , and submental spaces, and its relationships to the mylohyoid muscle. 93

Figure 21. 3D-CT images illustrate the relationship of right submandibular space infection to the adjacent soft and hard tissues. (Female patient, 31 y, Caucasian, LOS: 7.1). Lateral view of 3D-CT image shows the right masseter and part of medial pterygoid (green) and 3D reformatted of submandibular infection (blue) extend below the level of inferior border of the mandible and masseter muscle (a), lateral oblique view shows the location of infection (blue) from the lingual side in relation to the oblique ridge, mandibular foramen and hyoid bone (b), relationship of 3D submandibular infection (blue) to the associated muscles after change the contrast (c), display the spatial relationship between 3D lesion, MPM (green), LPM (red), and MM (green) (d). 94

Figure 22. 3D-CT images show polygonal shape of sever right submandibular infection across the midline and extends to the left side. (Male patient, 24y, African-American, LOS: 6.8 days). Swelling related to the lower right side (a), anterior view shows the 3D submandibular infection (blue) and masseter muscle (green) (b), posterior view demonstrates the 3D reconstructed muscles (green) and infection (blue) in relations to the mandible (c), lingual aspect after cropping, illustrated the infection (blue) and MPM (green ) in relation to the lingual aspect of mandible, styloid process, and hyoid bone (d). 95

Figure 23. 3D-CT images demonstrate the spatial relationship of the polygonal shape of the submandibular space infection and the adjacent structures. a. shows the relationship between the left submandibular space infection and anterior belly of diagastric and mylohyoid muscles. b. shows the relatioshipe between the polygonal shape of infection (blue) and submandibular gland, masseter, mylohyoid and geniohyoid muscles. c. shows enlargement of masseter and submandibular gland.

Chapter 4: Discussion

The main purpose of this project was to identify predictors of second operation, and hospital LOS. Different studies have reported that odontogenic infections commonly occur in the head and neck facial spaces, particularly in adults (Barton & Jane, 2003; Sato et al., 2009). Dental caries, periodontal disease, and operculum pericoronitis frequently lead to odontogenic infection. Submandibular space infections are commonly induced by infections in the second and third molar (Hasegawa et al., 2008; Obayashi et al., 2004;

Sato et al., 2009).

Submandibular infections may be life-threatening because they have the capability of rapidly spreading to further deep neck fascial spaces such as parapharyngeal, retropharyngeal, and carotid spaces (Ohshima et al., 2009). Severe forms of dental infections may lead to death as a result of acute airway narrowing (Hwang et al.,

2011). In this investigation, we retrospectively analyzed CT images of 103 patients with odontogenic infection in an attempt to establish 3D CT images of submandibular space infections and their associated muscles. Surgical intervention was implemented in all patients who participated in this project. However, some patients had a delayed recovery from the submandibular infection that lead to a second operation in order to establish pus drainage.

4.1 Clinical Data Investigation

Wang et al. (2005) reported that the majority of maxillofacial infections occur in adults and are very rare in children. The mean age of patients in this study is 39.3, and ranged between 18 to 73 years. Additionally, the maxillofacial infections were seen more frequently in individuals of the fourth and third decades of life (Figure 18). These results are in agreement with Kannangara et al., 1980 & Bahl et al., 2014 who mention that the odontogenic infections are more common in people of the third and fourth decade of age groups. Figure 24 shows that males and females are approximately equally distributed.

These findings are similar to Richard et al. (1991) who reported that the distribution of males and females with an odontogenic infection were the same, and their ages ranged between 23 to 70 years, with a mean of 43 years. Not only this, but also it was in agreement with Haug et al., (1991) who reported that the multispace infections were equally distributed among both sexes. In contrast, Uluibau et al. (2005) examined hospitalized and non-hospitalized patients with odontogenic infections, and found that males were more affected than females. They explained this gender difference in severity of infection based upon the fact that female patients maintained better oral hygiene.

Our study suggested that rates of infection were race-dependent. Figure 25 shows that Caucasian patients (n=76) had higher cases of infection than the African-Americans 98

(n=27). These results are consistent with Haug et al., (1991) who conducted their study in northeast Ohio and the ethnicity distribution among the odontogenic infections was

Caucasians (75%), African-American (23%), Hispanic (2%), and Asian (1%).

Morevover, the results of the current study is consistent with the statistical survey of population in Columbus on 2010 Census, the rate of Caucasions is 61.46% and

African-American is 27.98%. Our results regarding the race predilection reflect the normal distribution of the population in Columbus, Ohio. However, these findings appear contrary to Sato et al. (2009) who demonstrated that in three aspects: gender, the mean age of patients and race distribution. The average age of patients is 31 years, the gender distribution between males and females was 2:1, and the race of patients was equally distributed.

Figure 24. Distribution of males and females among maxillofacial infection.

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Figure 25. The diagram illustrates the rate of Caucasians and African- American patients.

Figure 26 shows the distribution of different systemic diseases among the different patients, and that the hypertension condition is the most frequently distrubted amongs the sample of study. However, in the elderly, the debilitating conditions are usually associated with severity of infections Zhang et al. (2010). This study shows that age is an important factor in complications of submandibular infection, and is significantly correlated with LOS (p=0.031). These findings are consistent with Zhang et al. (2010) who reported that the age is a potential factor associated with the life threatening conditions of deep neck infection. 101

Figure 26. The diagram shows the distribution of comorbidity among the different individuals.

4.2 Anatomical Location and Extent of Infection

Thorough understanding of head and neck anatomy is essential for evaluating deep neck infections and surgical intervention (Lypka & Hammoudeh, 2011). This project illustrates that facial spaces are not affected by infection in the same proportion, possibly due to the thickness of facial barriers not being the same between the different spaces, and the difference in proximity to the source of infection. The management of a

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submandibular space infection after it spreads to other deep neck space, remains bothersome due to the complexity of anatomy, and the unpredictability the extension of infections (Boscolo-Rizzo & Da Mosto, 2009).

4.3 Facial Space Infection

Figure 27 demonstrates the distribution of the deep fascial spaces that are involved with the spreading of submandibular infections. However, the submandibular space is not shown in the figure below because it‘s the primary location of infections in all CT images. The most common secondarily involved spaces around the mandible were the sublingual, masticator, and submental spaces, while the most common secondarily involved space that runs along the entire length of the neck was the parapharyngeal space. Our findings showed that multiple space infections are present in 90.3% of the patients , which is consistent with prior studies that report 93% of odontogenic infections involve multiple spaces (Rega et al., 2006; Yonetsu et al., 1998) In contrast, Bridgeman et al. (1995), who studied 107 infection cases, stated that 53% involved a single space.

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Figure 27. The diagram demonstrates the distribution of different facial spaces among the severty of infection.

Our study clearly indicates that odontogenic infections spread first to the submandibular space, located below the mylohyoid muscle, especially when the infection is located in one of the posterior mandibular teeth, as their root apices lie below the level of mylohyoid muscle. Single submandibular space infections were rare occurring in only

10 cases, whereas multiple space infections occurred in over 90% of the cases. These results indicate that submandibular space infections usually spread to the adjacent spaces,

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and may go as far as the mediastinal space. These results are consistent with other reports, which stated that an infection of the submandibular space is always accompanied by spreading of the infection to other deep neck facial spaces (Ariji et al., 2002; Boscolo-

Rizzo & Da Mosto 2009; Sato et al., 2009).

Figure 28. The diagram illustrates the left submandibular space infection (green arrows) at angle of the mandible.

The current study illustrates that different facial spaces are affected by infection in different proportions based on their proximity to the source of infection. Figure 29 shows the direct communication between the sublinual and submandibular space. The sublingual space is the second most involved space in patients with submandibular space 105

infections. This observation makes pragmatic sense, since there is no fascia to intervene between these two spaces, and both spaces communicate freely at the posterior border of the mylohyoid muscle (Harnsberger 1995; Kim et al., 1997; Yonetsu, 1998). Our findings indicate that infections may spread in either direction or may arise from the apices of teeth that are located above the level of the mylohyoid muscle attachment. Moreover, our study is in agreement with previous reports stating that narrowing of the airway and mediastinum infections are often associated with sublingual space infection (Boscolo-

Rizzo & Da Mosto, 2009).

Different investigators have found that the submandibular and buccal spaces are the most frequently involved fascial spaces in dental infections (Haug 1991; Labriola,

1983; Storoe, 2001). The results of our study were consistent with submandibular space involvement, while the percentage of buccal space involvement was only 17.5% with the sublingual, masticator, submental, and parapharyngeal (50.5%, 44.7%, 43.7%, and

32.0%) all being infected at a higher rate. Therefore, our results would suggest that the two most common sites of odontogenic infection occur in the submandibular and sublingual spaces followed by moderately highly rates of infection for the masticator and submental spaces and somewhat lower rates for the parapharygeal and buccal spaces.

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Figure 29. CT image demonstrates the sublingual and parapharyngeal spaces infections (green arrows) and direct communication with the submandibular space.

The submental space is located anterior to the submandibular space between the two anterior bellies of the digastric muscles, and an infection can reach this space via lower incisor teeth or through the free communication with the submandibular space.

When the infection is involved in the submandibular, sublingual spaces bilaterally, and the submental, the condition is called Ludwig‘s angina, which involves indurated swelling accompanied with elevation of the tongue and compromise of the airway

(Barton & Jane, 2003; Lypka & Hammoudeh, 2011). Airway narrowing was present in

15 of the 20 patients that had both submental and sublingual space infection (75%) making our study consistent with prior observations. 107

The masticator space is a deep facial space bounded by the two layers of the superficial fascia of the deep cervical fascia that split at the inferior border of the body of the mandible. It contains the four muscles of mastication and all of the branches of the mandibular division of the trigeminal nerve (Hasegawa et al., 2008). 46 of the 103 patients showed masticatory space involvement in addition to the submandibular space infection (44.7%). Many of these cases displayed enlargement of the masseter and medial pterygoid muscles.

Prior studies (Ariji et al., 2002; Wabik et al., 2014) have reported that odontogenic infections often spread first to the masticator space then, via the masseter and medial pterygoid muscle, pass downward to the submandibular space. However, the superficial layer of the investing fascia that envelops those muscles is thick and can act as a barrier to prevent the spread of infection. Therefore, the incidences of submandibular infection should not be high. Our data contradicts these observations and agrees with other studies that have shown that masticator space infections are very often accompanied by submandibular involvement [Hardin et al., 1985; Marioni et al., 2008; Yonetsu et al.,

1998]. In these studies, the odontogenic infection spreads from the mandibular teeth to the submandibular space, which in turn connects with the masticator space, rather than from masticator space to the submandibular space.

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The PPS is cone shaped and extends from the base of the skull to the level of the hyoid bone. It is bounded laterally by the medial pterygoid muscle, which is covered by the superficial layer of the deep cervical fascia, and medially by the superior constrictor muscle covered by buccopharyngeal fascia. This space gets infected as a direct extension from the adjacent facial spaces. In particular, the masticator space is known to spread to the retropharyngeal space from which it can gain access to the mediastinum (Barton &

Jane, 2003; Lypka & Hammoudeh, 2011).

Our results are in agreement with these reports with PPS infections having a high rate of secondary masticator space infections. Our observations suggest that the infection extends directly from the masticator space (Figure 30). However, the infection may also spread directly from the submandibular space to the parapharyngeal space because the fascial barrier between these spaces is thin (Harnsberger, 1995; Kim et al., 1997). Patient

3 (Figure 31) supports this hypothesis as there is a direct communication between the submandibular and parapharyngeal space infections (Harnsberger 1995; Peterson 1993;

Yonetsu 1998). However, our study also indicates that 10% of parapharyngeal space infections occur without masticator space involvement with only 22% of our patients having parapharyngeal and masticator space infection.

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Figure 30. Axial CT image shows collection of fluid within the right parapharyngeal space (arrow). The size of the right medial pterygoid muscle is larger than the left one (arrow head).

Figure 31. Axial CT image shows the parapharyngeal abscess (arrow) in communication with the submandibular infection (arrowheads) with displacement of the air way.

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The RPS extends from the base of the skull to the level of the second thoracic vertebra. 5.8% of our patients had retropharyngeal abscesses with most of cases also suffering from parapharyngeal space infections. These findings are in agreement with

Kato et al. (2001), who suggested that the source of RPS infection comes from the PPS.

Barton and Jane (2003) reported that patients who have RPS infections are more frequently at risk of airway compromise. However, in their study only 5 out of 20 patients with RPS infections complained of airway narrowing. These observations are not in agreement with our results, which suggest a much higher incidence of RPS and PPS co-involvement.

The DS is located behind the retropharyngeal space, between the alar fascia anteriorly and prevertebral fascia posteriorly, making it difficult to differentiate from RPS radiologically. However, infections of the RPS can reach down to the level of second thoracic vertebra, whereas infections of the DS infection may extend to the level of the diaphragm (Barton & Jane, 2003). In our study, only one DS infection was reported. The manifestation of the gas bubbles indicates the development of an abscess induced by anaerobic micro-organism (Barton & Jane, 2003). In our investigation, DS infections can‘t be used, to predict any one of the two outcome variables.

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Prior studies suggest that CT is a reliable tool to distinguish between abscess and cellulitis formation with an accuracy as low as 76% (McRae et al., 1993; Nyberg et al.,

1985; Hudgins, 2000; Ungkanont et al., 1995; Lazor et al., 1994). An abscess is defined as a drainable cavity of infected debris, while edematous, infected tissue that has not yet necrosed is called phlegmon (Barton & Jane, 2003). Because of the close appearance between phlegmon and cellulitis on CT analysis, I defined this group of infections as cellulitis. Even the ability to differentiate between an abscess and cellulitis on CT is difficult and dependent upon the experience level of the radiologist. An abscess on CT appears as a low attenuation area surrounded by a scalloped or circumferential rim of enhancement. In contrast, the margins associated with cellulitis would be a thin or poorly defined rim surrounding the area of low attenuation (Boscolo-Rizzo et al., 2009). In our study, 59.2% of the patients presented with an abscess and 40.8% with cellulitis. Abscess development showed a strong correlative link to airway narrowing, which is consistent with prior studies showing that airway compromise in these patients may be due to aspiration of abscess or displacement of the airway by abscess formation (Wills &

Vernon, 1981).

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Hypertension was the most frequent comorbidity in our investigation and it has been used as a predictor for outcome variables. Our study indicated that hypertension had no correlation with secondary operative procedures or hospital LOS. On the other hand, the second most common comorbidity in our study was diabetes. Many investigators have reported that the diabetes is one of the most common debilitating conditions associated with dental infections (Boscolo et al., 2006; Kato et al., 2001; Ridder et al.,

2005). It is postulated that the development of diabetic-induced hyperglycemia interferes with the function of white blood cells and impairs their defense capability (Eisler et al.,

2013). Even so, the results of our study did not show any relationship between diabetes and the two outcome variables examined.

Prior studies have indicated that renal disease appears to correlate with the severity of deep neck infections (Bottin et al., 2003; Har et al., 1994; Huang et al., 2004).

However in our study, renal disease showed no significance in association with the severity of infection or any of the two outcome variables. This discrepancy can explain the few number of patients who complained of renal insufficiency disease (3 patients of

103).

Cancer has also been reported as a positive comorbidity for the development of severe infections in older people (Boscolo et al., 2006). It is postulated that the various immunosuppressive drugs administered during cancer treatment permit the development 113

of opportunistic infections. In our study, the longest hospital LOS was associated with a patient diagnosed with cancer, hypertension and deep neck infection. However, the other cancer patients in our study had an average hospital LOS between 3 to 5 days. Therefore, our results did not confirm a strong correlation between the presence of cancer and our identified outcome variable.

4.4 Consequencies of the Submandibular Space Infection

Odontogenic infections can lead to many complications such as thrombophlebitis of the retromandibular vein, which in turn may lead to cavernous sinus thrombosis via pterygoid venous plexus (Ferrera et al., 1996). Mediastinitis is one of the most serious and life-threatening complications of odontogenic infections (Lee et al., 2007). Pinto et al. (2008) suggest that the spread of odontogenic infections to the mediastinal space can occur through three routs: (1) front of the viscera (anterior visceral space), which frequently spreads the infection to the anterior mediastinum; (2) lateral to the viscera

(PPS), to the middle mediastinum, (3) posterior to the viscera (RPS, or DS), to the posterior mediastinum.

In our study, 6.8% of the patients presented with mediastinitis. One patient had diabetes and renal insufficiency as comorbidities with three others listed as having hypertension. Most of the patients suffering from mediastinitis had secondary sites of infection that were most commonly found in the PPS an RPS. Our results showed no 114

significant correlation between the development of mediastinitis and our chosen outcome variables. Wang et al. (2010) suggested that deep neck infections are usually associated with older and unhealthy patients, which was not the case in our study where gender, race and age showed no significant linkages to mediastinitis or any of the observed comorbidities listed in our study.Their sample size was 439 individual which can explain the significant relationship between the unhealthy elderly patients and deep neck infections. Our findings are consistent with the observations of Dalla et al., (2013), who reported that deep neck infections are observed across the spectrum of healthy/sick and young/old patients.

Daramola et al. (2009) have shown that airway narrowing and displacement is one of the most common complications of deep neck infections including death. Compromise or restriction of the airway is often induced by inflammation and edema but can also be caused by aspiration of an abscess material (Barton & Jane, 2003; Wills & Vernon,

1981). 19.4% of cases in our study had narrowing of the airway and this complication showed a significant correlation with hospital LOS. This observation is likely the result of the fact that complaint of airway restriction results in hospital admission due to its potential life-threatening complications. An unexpected occurrence in our study was that patients complained of air way restrictions without ever being exposed to endotracheal

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. However, this can be explained by the fact that some hospitals prefer not to expose their patients to the process of intubation as it can further jeopardize their health.

Trismus is defined as difficulty in opening of the mouth and it has been previously reported to be an important symptom of masticatory space infection (Aruiji et al., 1991; Hardin et al., 1985). Trismus can also be induced by tumor growth close to the temporomandibular joint (TMJ) (Rapidis et al., 2015). If masticatory space infections are not treated properly and early, they may lead to serious life-threatening conditions such as cervical cellulitis or descending mediastinitis (Dhanrajani & Jonaidel, 2002). Although

44.7% of the patients in our study presented with masticatory space infections, only 6.8% were identified with trismus. Our data differs that reported by Dhanrajani & Jonaidel

(2002), who stated that masticator space infection frequently leads to restriction in opening of the mouth. When we looked for a relationship between trismus and our two outcome variables, it did not show significant relationship with any one of them.

Airway narrowing represents one of the worst consequencies associated with odontogenic infections. Zhang et al. (2010) reported that odontogenic-based respiratory obstruction was usually due to an infection of the submandibular, sublingual, submental, or parapharyngeal spaces. He suggested that close monitoring is needed for these patients because they may require an emergency endotracheal intubation or tracheotomy due to

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respiratory distress. In our study, we found a strong correlation between airway obstruction and infections associated with the submandibular spaces.

Our data indicates that patients who suffered from airway narrowing have one or more of the following spaces involved: the submandibular, sublingual, submental, parapharyngeal, and/or masticator spaces. These findings were in agreement with Zhang et al. (2010), who observed that when one or more of the above mentioned spaces is infected, close monitoring of the patient is required to prevent air way obstruction.

Our study indicates that the likelihood of patients having a second operation is significantly correlated with infections in the submental or retromolar trigone spaces. Our explanation for these results is that the submental space lies in the midline of the neck, so the infection may spread to the opposite side complicating unilateral drainage of the infection. The other probability for the significant correlation between the submental space infections and a second operation is that the submental space may lead to involvement of the sublingual and submandibular spaces bilaterally, which indicative of a serious condition is known as Ludwig‘s angina. Lypka & Hammoudeh (2011) stated that an understanding of the anatomy and relationship of the different facial spaces of the head and neck region help in providing information about the spread of infection and its surgical management. Kato et al. (2001) reported that odontogenic infections spread to the PPS and RPS via the pterygomandibular space through the submandibular space. 117

Based upon these observations, I can say that the spreading of an infection from the retromolar triangle to the pterygomandibular space leads to multiple abscess formation that in turn leads to difficulty in clinical management of the infection.

In the previous studies of the odontogenic infections, the following clinical variables were used as predictors for hospital LOS: certain medical diseases, temperature, lower face infection, admission white blood cell count, location of infection, time spent in the operating room, and infection of non-third molar posterior teeth (Dodson et al., 1991;

Huang et al., 2004; Peters et al., 1996). In our study, we determine some of the clinical variables such as facial spaces, characteristic of infection, complications, and demographics as well as the 3D volume of submandibular infection, 3D volume of associated muscles, spatial relationship of a submandibular infection to the 3D volume rendering image, and 3D density of lesions are also able to predict hospital LOS.

Our study did not find a significant correlation between immunocomprimised patients (cancer and diabetes) and hospital LOS. This result is in inconsistent with Ylijoki et al. (2001), who reported that the lengthy stays in hospital occur as a result of deep facial infections associated with immunosuppressive conditions. This difference may be due to the small sample size of these patients in our study as suggested by Carey and

Dodson (2001) and Ylijoki et al. (2001), who also reported the lack of a significant

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correlation between immunocompromised patients and hospital LOS due to small sample size.

Prior studies have reported that the deep neck infection (i.e., parapharyngeal, retropharyngeal, and pretracheal) significantly predicted hospital LOS (Dodson et al.,

1991; Grodinsky & Holyoke, 1938; Peters et al., 1996). Our results do not support this observation as only the involvement of submental space infections and airway narrowing were significantly correlated with hospital LOS. This result is also in contrast to that reported by Flynn et al. (part 2, 2006), which suggested that the graded severity score of an infection is dependent upon its proximity to the airway. Submental space infections are therefore only classified as moderately severe. In addition, the submental space is one of the spaces that may be involved in Ludwig‘s angina, a serious condition of infection involving the submandibular, sublingual, and submental spaces bilaterally.

4.5 3D Volume of Submandibular Infections

To our knowledge, this study represents the first analysis of submandibular infections using 3D-CT reformatted images. Swelling is the common sign of patients complaining of submandibular space infection which can be decreased in size after surgical drainage. According to Adekeye and Adekeye, 1982 who reported that the swelling can be reduced in size after 7th day of incision and drainage.

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Because the maxillofacial surgeons correlate the size of swelling of odontogenic infections with the severty of infection, I attempted in this study to measure the volume of infection to use it as predictor for the comorbidity. However, our results suggest that there is no relationship between the 3D volume of a submandibular infection and the clinical outcome variables. This lack of significance could be due to the total number of segmented submandibular infections quantified in the study due to a lack of clear areas of attenuation and/or the enhancement rims. Because no previous studies exist for the 3D-

CT analysis of submandibular space infections, further investigation of this lesion type with a larger sample size may be needed to evaluate the 3D parameters that affect clinical outcome. Even so, determination of the submandibular infection volume before surgery using 3D-CT imaging provides the surgeon with a baseline volume that can be compared to post-surgical images and volume calculations to assess proper drainage of the site.

4.6 3D Muscular Relationships

One of the objectives of this study was to measure the volume of muscles on the affected and unaffected side, and compare the two to determine of muscle volume represents a predictor of clinical outcome. The difference between the volumes of the masseter and medial pterygoid muscles on the affected side versus the unaffected side was statistically significant. However, the difference in volumes between the lateral pterygoid muscles of both sides was not significant. This implies that the masticator 120

space is liable to be infected whenever the submandibular space is involved. Furthermore, the infection is only apparent in the lower part of the masticator space, which explains why the lateral pterygoid muscles, on both sides, are not involved in the infection since they‘re situated on the upper half of the masticator space.

On the other hand, the 3D volume of the infected masseter muscles was not significantly correlated with the three outcome variables, while the volume of infected medial pterygoid muscles exhibited a strong correlation with hospital LOS. Interestingly, the volume of the infected lateral pterygoid muscle showed significant correlation with hospital LOS.

4.7 Spatial Relationship of ROI with Adjacent Area

Because of complexity of the anatomy and landmarks that are involved in head and neck infections, the course of infection is often difficult to predict. This in turn may delay the appropriate evaluation and management of the infection which in turn increases the risk of life- threatening conditions. Currently, 2D visualization of head and neck structures by different types of imaging plays a central role in determining the extent of infections for diagnostic purposes. Contrast-enhanced CT is one of the modalities of choice for determining the specific path of infections (Hegde et al., 2012). In this study, our strategy was to define the spatial relationship of submandibular infections to the associated structures through 3D-CT image analysis. 121

As illustrated in Figure 19, the relationship between the 3D image of the infection and the associated muscles are clearer than on 2D image. The different images in this figure show a clear relationship between the polygonal model of infection and other adjacent structures such as the posterior and inferior borders of mandible, and condyle process. Moreover, the location of abscess is more obvious in the medial and lateral sides of the body of mandible which indicates the involvement of pterygo-mandibular and submasseteric spaces. In contrast, the 2D image (Figure 32) shows the areas of attenuation without clear spatial relationship to adjacent structures and doesn‘t permit examination of the lesion from alternative views.

During the process of surgical planning, the surgeon should have sufficient orientation with the field of operation. Figure 20 shows that the precise anatomic location of an odontogenic lesion and its relationship to the surrounding structures can be obtained using 3D-CT image analysis. In a similar manner, Figure 20 (c & d) shows the spatial relationship between the polygonal submandibular infection and other anatomical structures can be identified by changing the image contrast. For example, the branches of common carotid artery (external and internal), mylohyoid muscle and posterior belly of digastric muscle can all be observed.

Figure 21 shows examples of polygonal shapes for the muscles of mastication associated with a submandibular infection. The lateral location of polygonal shape of 122

masseter muscle and its relationship to the mandible and the submandibular infection can be easily identified in panel (a). The location and shape of the submandibular infection can also be checked from another aspect, the lingual side as shown in panel (b). This view allows us to see the relationship of the polygonal lesion to the mandibular canal and oblique ridge in a similar manner as observed by Friedland et al. (2008). Finally, 3D-CT provides excellent visualization for the spatial relationship of a segmented submandibular space infection and other adjacent structures. Our results are similar to prior studies that have demonstrated that 3D-CT images can identify the precise anatomical location of odontogenic lesions and their surrounding structures thereby improving surgical planning, reducing the time of operation and improving the surgical outcome.

123

(a)

(b) Figure 32. Axial CT reveals a masticator space abscess. a. Shows rim-enhancing fluid is existing within the left medial pterygoid muscle in the lingual surface (blue arrows). b.The left masseter muscle is enlarged and edematous (yellow arrow heads). A rim- enhancing fluid collection is occurring within the left masseter (green arrows). The left medial pterygoid muscle is enlarged and edematous (red arrow heads). 124

Limitations

There are several limitations associated with our study, First, is the thickness of the CT images used in our retrospective study. The suitable thickness for high-quality 3D rendering of images ranges between 1 to 1.5 mm. However, the thickness of the CT images used in our study ranged between 2.5 to 3.0 mm, which in turn affected the overall resolution of 3D renderings. Second, 3D imaging reconstruction is labor intensive because it needs to be manually segmented.

Third, the total number of participants was 103, but the number of CT images that showed the area of attenuation with the rim of enhancement and underwent segmentation of submandibular space infection was only 83. There were 20 patients who did not undergo segmentation of the submandibular lesion, which in turn may have affected our statistical analyses and determination of outcome variables. Fourth, the clinical data for some of the patients in the study was incomplete or hard to interpret, which may have affected our understanding of their clinical complications.

Fifth, a proper determination of abcess versus cellulitis requires clinical examination and contrast enhanced CT. For the purpose of our study, we were solely dependent upon the contrast CT images, Miller et al. (1999) have reported as being only

89% accurate. Sixth, the age range for our study was extremely broad (i.e., 18 to 73 years old) limiting our ability to draw any age-based conclusions to the study. Finally, the way 125

that our data was collected may not be appropriate for every type of variable, and prospective studies should therefore be carried out to confirm our findings

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Chapter 5: Conclusions

Odontogenic infections can become hazardous if they spread to deep fascial tissues within the head and neck. The submandibular space is the most common odontogenic infection and its spread can be unpredictable do to the complex nature of the associated anatomy. Clearly, gaining a better understanding of the anatomic location of odontogenic lesions and their involvement with the surrounding structures is needed.

Such information should improve the diagnosis, management, and treatment of head and neck infections thereby improving their overall clinical outcoume.

This study describes a 3D image analysis procedure for the assessment of submandibular space infections and their associated muscles using contrast enhanced CT imaging. Our results indicate that the most common facial spaces infected in descending order of prevelance included the sublingual, submental, masticator, parapharyngeal, retropharyngeal, carotid, and mediastinum spaces. Although the total volume of a submandibular space infection and the involved muscles (3DIM, 3DILP and 3DIMP) showed no correlation with our outcome variables, second operation and hospital LOS, these negative results suggest that the volume of infection and affected muscles can‘t be used as predictors in determining the severity of submandibular space infections.

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Gender, age, and most disease comorbidities had no correlation with the severity of submandibular infections. Caucasians were significantly over-represented in our patient population suggesting that race may be a factor in the development of submandibular infections. However, this observation may simply reflect the local demographics of our hospital study and hence requires further investigation. Although the number of patients who were intubated in our study was not high, airway narrowing was one of the most serious complications observed followed by the development of, mediastinitis and trismus.

Summary

This study represents the first time that 3D-CT reformatted images have been used to assess submandibular space infections in an effort to identify potential novel diagnostic and prognostic markers. Our results indicate that this approach provides a much better overall picture of the size and relationship of the lesion to surrounding structures including its spread to associated facial spaces. During our assessment of outcome variabless, we determined that the 3DILP and 3DIMP muscle volume on the infected side were the best predictors of infection severity and hospital length of stay.

Additional studies are needed to confirm and extend these observations including prospective studies, increased sample size and the utilization of thin slice CT images. In conclusion, the results of this novel study indicate that 3D-CT renderings can be a benefit 128

in the diagnosis and presurgical planning for the treatment of submandibular space infections and should be considered as a functional part of the clinical management of these important odontogenic lesions.

129

References

Adekeye EO and Adekeye JO. The pathogenesis and microbiology of idiopathic cervicofacial abscesses. J Oral Maxillofac Surg. 1982; 40(2): 100-106.

Ahmad N, Abubaker AO, Laskin DM, Steffen D. The Financial Burden of Hospitalization Associated With Odontogenic Infections. J Oral Maxillofac Surg. 2013; 71(4):656-658.

Rosset A, Spadola L, Ratib O. OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images. Journal of Digital Imaging.2004; 17(3): 205– 216.

Aoyagi H, Iwasaki S, Nakamura K. Three-dimensional observation of mouse tongue muscles using micro-computed tomography. Odontology. 2015; 103(1): 1– 8.

Arias-Irimia O, Barona Dorado C, Gomez Moreno G, Brinkmann JC, and Martinez- Gonzalez JM. Pre-operative measurement of the volume of bone graft in sinus lifts using CompuDent. Clin. Oral Impl. Res. 2012; 23(9): 1070-1074.

Ariji Y., Gotoh M., Kimura Y., Naitoh M., Kurita K., Natsume N., and Ariji E. Odontogenic infection pathway to the submandibular space: imaging assessment. Int. J. Oral Maxillofac. Surg. 2002: 31(2): 165–169.

Aruiji E, Moriguchi S, Kuroki T, Kanda S. Computed tomography of maxillofacial infection. Dentomaxillofac Radiol. 1991; 20(3): 147–151.

Bahl R, Sandhu S, Singh K, Sahai N. Gupta M. Odontogenic infections: Microbioloby and management. Contemp Clin Dent. 2014; 5(3): 307-311.

Barton FB, Jane LW. Infection of the facial area, oral cavity, oropharynx, and retropharynx. Neuroimaging Clinics of North America. 2003; 13(3): 393–410.

Boeddinghaus R & Whyte A. Current concepts in maxillofacial imaging. European Journal of Radiology. 2008; 66(3): 396–418.

130

Boscolo-Rizzo P, Da Mosto MC. Submandibular space infection: a potentially lethal infection. International Journal of Infectious Diseases. 2009; 13(3): 327–333.

Boscolo-Rizzo P, Marchiori C, Zanetti F, Vaglia A, Da Mosto MC. Conservative management of deep neck abscesses in adults: the importance of CECT findings. Otolaryngol Head Neck Surg. 2006; 135(6):894–899.

Bottin R, Marioni G, Rinaldi R, Boninsegna M, Salvadori L, Staffieri A.. Deep neck infection: a present day complication. A retrospective review of 83 cases (1998-2001). Eur Arch Otorhinolaryngol. 2003; 260(10):576–579.

Bridgeman A, Wiesenfeld D, Hellyar A, Sheldon W. Major maxillofacial infections. An evaluation of 107 cases. Aust Dent J. 1995; 40(5):281–288.

Bucsko JK.Open-source viewer opens doors—OsiriX unlocks Mac platform to imaging. Radiol Today. 2007; 8(3):12.

Carey JW, Dodson TB. Hospital course of HIV-positive patients with odontogenic infections. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2001; 91(1):23–27.

Carter BW, Tomiyama N, Bhora FY, Rosado de Christenson ML, Nakajima J, Boiselle PM, Detterbeck FC, Marom EM. A modern definition of mediastinal compartments. J Thorac Oncol. 2014; 9(2): 97–101.

Cavalcanti MG, Ruprecht A, Bonomie JM, Vannier MW. The validation of 3D spiral CT- based measurements of simulated maxillofacial neoplasms. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, Endodontic. 2000; 89(6): 753– 758.

Chen W, Wang J, Xin W, Peng Y, Xu Q. Accuracy of 16-row multislice computed tomographic angiography for assessment of small cerebral aneurysms. Neurosurgery. 2008; 62(1):113–121.

Clauser P, Londero V, Como G, Girometti R, Bazzocchi M. Zuiani C. Comparison between different imaging techniques in the evaluation of malignant breast lesions: can 3D ultrasound be useful? Radiol med. 2014; 119(4):240–248.

131

Dalla TD, Brunold S, Kisielewsky I, Kloss FR., Burtscher D. Life-threatening complications of deep neck space infections. Wien Klin Wochenschr. 2013; 125(21–22): 680–686.

Daramola OO, Flanagan CE, Maisel RH, Odland RM. Diagnosis and treatment of deep neck space abscesses. Otolaryngology - Head Neck Surgery. 2009; 141(1):123–130.

DeMarino DP, Steiner E, Poster RB, Katzberg RW, Hengerer AS, Herman GT, Wayne WS, Prosser DC. Three-dimensional Computed Tomography in Maxillofacial Trauma. Arch Otolaryngol.1986; 112(2):146–150.

De Vicente Rodriguez JC. Celulitis maxilofaciales. Med Oral Patol Oral Cir Bucal. 2004; 9(Suppl: 133–138):126–133.

Dhanrajani PJ, Jonaidel O. Trismus: Aetiology, Differential Diagnosis and Treatment. Dent Update. 2002; 29(2): 88–94.

Dodson TB, Barton JA, Kaban LB. Predictors of outcome in children hospitalized with maxillofacial infections: A linear logistic model. J Oral Maxillofac Surg. 1991; 49(8): 838–842.

Doweck I, Denys D, Robbins KT. Tumor Volume Predicts Outcome for Advanced Head and Neck Cancer Treated With Targeted Chemoradiotherapy. Laryngoscope. 2002; 112(10):1742–1749.

Eisler L, Wearda K, Romatoski K, Odland RM. Morbidity and Cost of Odontogenic Infections. Otolaryngology -- Head and Neck Surgery. 2013; 149(1): 84– 88.

El-Baz A, Nitzken M, Khalifa F, Elnakib A, Gimel‘farb G, Falk R, and Abo El-Ghar M. 3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules. Information Processing in Medical Imaging. 2011; 22: 772–783.

Elizabeth J Rosen, 2002. Deep neck spaces and infections. Grand Rounds Presentation, UTMB, Dept. of Otolaryngology.

El-Sayed Y, and Al Dousary S. Deep-neck space abscesses. J Otolaryngol. 1996; 25(4): 227–233. 132

Ferrera PC, Busino LJ, and Snyder HS. Uncommon complications of odontogenic infections. Am J Emerg Med. 1996; 14(3):317–322.

Flynn TR, Shanti RM, Levi MH, Adamo AK, Kraut RA., Trieger N. Severe dontogenic Infections, Part 1: Prospective Report. J Oral Maxillofac Surg. 2006; 64(7): 1093 –1103.

Flynn TR, Shanti RM, Hayes C. Severe Odontogenic Infections, Part 2: Prospective Outcomes Study. J Oral Maxillofac Surg. 2006; 64(7): 1104–1113.

Friedland B, Donoff B, Dodson TB. The use of 3- dimensional reconstructions to evaluate the anatomic relationship of the mandibular canal and impacted mandibular third molars. J Oral Maxillofac Surg. 2008; 66(8): 1678–1685.

Furuya K, Murayama S, Soeda H, Murakami J, Ichinose Y, Yabuuchi H, Katsuda Y, Koga M, Masuda K. New classification of small pulmonary nodules by margin characteristics on high-resolution CT. Acta Radiol. 1999; 40(5): 496–504.

Glasier CM, Stark JE, Jacobs RF, Mancias P, Leithiser RE Jr, Seibert RW, Seibert JJ. CT and ultrasound imaging of retropharyngeal abscesses in children. AJNR Am J Neuroradiol. 1992; 13(4): 1191–1195.

Goto TK, Tokumori K, Nakamura Y, Yahagi M, Yuasa K, Okamura K, Kanda S. Volume changes in human masticatory muscles between jaw closing and opening. J Dent Res. 2002; 81(6): 428–432.

Goto TK, Nishida S, Yahagi M, Langenbach GEJ, Nakamura Y, Tokumori K, Sakai S, Yabuuchi H, Yoshiura K: Size and orientation of masticatory muscles in patients with mandibular laterognathism. J Dent Res. 2006; 85(6): 552–556.

Greess H, Nömayr A., Tomandl B, Blank M, Lell M, Lenz M, Bautz W. 2D and 3D visualisation of head and neck tumours from spiral-CT data. European Journal of Radiology. 2000; 33(3): 170–177.

Grodinsky M, Holyoke EA. The fasciae and fascial spaces of the head, neck, and adjacent regions. Am J Anat. 1938; 63(3): 367–408.

133

Hamza NS, Farrel J, Strauss M, Bonomo RA. Deep fascial space infection of the neck: a continuing challenge. Southern Medical Journal. 2003; 96 (9): 928–932.

Hardin CW, Harnsberger HR, Osborn AG, Doxey GP, Davis RK, Nyberg DA. Infection and tumor of the masticator space: CT evaluation. Radiology. 1985; 157(2): 413–417.

Har-El G, Aroesty JH, Shaha A, Lucente FE. Changing trends in deep neck abscesses. Oral Surg Oral Med Oral Pathol. 1994; 77(5): 446–450.

Harnsberger HR. The oral cavity: emphasizing the sublingual and submandibular spaces. Handbook of Head and Neck Imaging. 2nd edn. St Louis: Mosby Year Book, Inc. 1995; 120–149.

Harnsberger HR. The parapharyngeal space and the pharyngeal mucosal space. Handbook of Head and Neck Imaging. 2nd edn. St Louis: Mosby Year Book, Inc. 1995; 29–45.

Hasegawa T, Shibuya Y, Kuroki S, Takeuchi J, Yokoo S, Umeda M, Komori T. Two Cases Of Masticator Space Abscess Initially Diagnosed As Temporomandibular Joint Disorder. Kobe J. Med. Sci. 2008; 54(3): 163–168.

Hassfeld S, Brief J, Raczkowsky J, Marmulla R, Mende U, Ziegler C. Computer-based approaches for maxillofacial interventions. Min Invas Ther & Allied Technol. 2003; 12 (1–2): 25–35.

Haug RH, Hoffman MJ, Indresano AT. An epidemiological and anatomic survey of odontogenic infections. J Oral Maxillofac Surg. 1991; 49(9): 976–980.

Hegde AN, Mohan S, Pandya A, Shah GV. Imaging in Infections of the Head and Neck. Neuroimaging Clinics of North America. 2012; 22 (4): 727–754.

Holt GR, McManus K, Newman RK, Potter JL, Tinsley PP.Computed tomography in the diagnosis of deep neck infections. Arch Otolaryngol. 1982; 108(11): 693–696.

Hopper KD, Iyriboz AT, Wise SW, Neuman JD, Mauger DT, Kasales CJ. Mucosal detail at CT virtual reality: surface versus volume rendering. Radiology. 2000; 214(2): 517–522. 134

Hu Y, Li H, Qiao G, Liu H, Ji A, Ye F. Computer-assisted virtual surgical procedure for acetabular fractures based on real CT data.Injury, Int. J. Care Injured. 2011; 42(10): 1121–1124.

Huang TT, Liu TC, Chen PR, Tseng FY, Yeh TH, Chen YS. Deep neck infection: Analysis of 185 cases. Head Neck. 2004; 26(10): 854–860.

Hudgins PA. Nodal and nonnodal inflammatory processes of the pediatric neck. Neuroimaging Clin N Am. 2000; 10(1): 18–192.

Hwang T, Antoun JS, and Lee KH. Features of odontogenic infections in hospitalised and non-hospitalised settings. Emerg Med J. 2011; 28(9): 766–769.

Iwano S, Nakamura T, Kamioka Y, Ishigaki T. Computer-aided diagnosis: A shape classification of pulmonary nodules imaged by high-resolution CT. Computerized Medical Imaging and Graphics. 2005; 29(7): 565–570.

Johnson PT, Fishman EK, Duckwall JR, Calhoun PS, Heath DG. Interactive three- dimensional volume rendering of spiral CT data: current applications in the thorax. Radiographics. 1998; 18(1): 165–187.

Jundt JS, and Gutta R. Characteristics and cost impact of severe odontogenic infections. Oral Surg Oral Med Oral Pathol Oral Radiol. 2012; 114(5): 558–566.

Kaluskar S, Bajaj P, Bane P. Deep space infections of neck. Indian Journal of Otolaryngology and Head and Neck Surgery. 2007; 59(1): 45–48.

Kannangara DW, Thadepalli H, McQuirter JL. Bacteriology and treatment of dental infections. Oral Surg Oral Med Oral Pathol. 1980;50(2):103-109.

Kato Y, Shibata T, Yamashita T, Kobayashi A, Yonemoto K, Makita H, Mizuta K, Hayashi S, Toida M. Parapharyngeal and Retropharyngeal Space Infection of Odontogenic Origin. Asian J Oral Maxillfac Surg. 2001; 18(3): 224–227.

135

Kawata Y, Niki N, Ohmatsu H, Eguchi K, & Moriyama N. Shape analysis of pulmonary nodules based on thin section CT images. Medical Imaging. 1997; 3034: 964–974.

Kim E, Jeon JH, Shim YH, Lee KS, Kim SY, Kim ER Masticator space abscess in a 47- day-old infant. Korean J Pediatr. 2011; 54(8): 350-353.

Kim HJ, Park ED, Kim JH, Hwang EG, Chung SH. Odontogenic versus nonodontogenic deep neck space infections: CT manifestations. J Comput Assist Tomogr. 1997; 21(2): 202–208.

Kim MK, Nalliah RP, Lee MK, Allareddy V. Factors associated with length of stay and hospital charges for pateints hospitalized with mouth cellulitis. Oral Surg Oral Med Oral Pathol Oral Radiol. 2012; 113(1): 21–28.

Kishi K, Akita K, Nakatsu T, Adachi K, Uemura M, Ochi S, Arita ES.. Evaluation of inflammatory diseases of jaw bones with three-dimensional CT imaging: comparison with conventional radiography and high-resolution CT. Oral Radiology.1992; 8 (2): 99– 109.

Labriola JD, Mascaro J, Alpert B. The microbiologic flora of orofacial abscesses. J Oral Maxillofac Surg. 1983; 41(11): 711–714.

Larawin V, Naipao J, Dubey SP. Head and neck space infections. Otolaryngology- Head and Neck Surgery. 2006; 135(6): 889–893.

Lazor JB, Cunningham MJ, Eavey RD, Weber AL. Comparison of computed tomography and surgical findings in deep neck infections. Otolaryngol Head Neck Surg. 1994; 111(6): 746–750.

Lee JK, Kim HD, Lim SC. Predisposing factors of complicated deep neck infection: an analysis of 158 cases. Yonsei Med J. 2007; 48(1): 55–62.

Levitt GW. Cervical fascia and deep neck infections. Laryngoscope. 1970; 80: 409-433.

Lindner HH. The Anatomy of the Fasciae of the Face and Neck with Particular Reference to the Spread and Treatment of Intraoral Infections (Ludwig's) that Have Progressed into Adjacent Fascial Spaces. Ann. Surg. 1986; 204(6): 705– 714. 136

Lypka M, & Hammoudeh J. Dentoalveolar infections. Oral Maxillofac Surg Clin North Am. 2011; 23(3): 415–424.

Marioni G, Rinaldi R, Staffieri C, Marchese-Ragona R, Saia G, Stramare R, Bertolin A, Dal Borgo R, Ragno F, Staffieri. Deep neck infection with denta origin: analysis of 85 consecutive cases (2000-2006). Acta Oto-Laryngologica. 2008; 128(2): 201–206. Marsh JL & Vannier MW. The ―third‖ dimension in craniofacial surgery. Plast. Reconstr. Surg. 1983; 71(6): 759–767.

Maeda M, Katsumata A, Ariji Y, Muramatsu A, Yoshida K, Goto S, Kurita K, Ariji E. 3D-CT evaluation of facial asymmetry in patients with maxillofacial deformities. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2006; 102(3): 382–390.

Matt BH, Lusk RP. Delineation of a deep abscess neck with magnetic resonance imaging. Ann Otol Rhinol Laryngol.1987; 96(5): 615–617.

McRae D, Dilkes M, Jacob-Hood J, Ghufoor K. Computerized tomography of acute, tender peritonsillar swellings. Clin Otolaryngol.1993; 18(5): 350–354.

Melissano G, Bertoglio L, Civelli V, Amato AC, Coppi G, Civilini E, Calori G, De Cobelli F, Del Maschio A, Chiesa R. Demonstration of the Adamkiewicz Artery by Multidetector Computed Tomography Angiography Analysed with the Open-Source Software OsiriX. Eur J Vasc Enovasc Surg.2009; 37(4): 395–400.

Miller WD, Furst IM, Sandor GK, Keller MA. A prospective blinded comparison of clinical examination and computed tomography in deep neck infections. Laryngoscope. 1999; 109(11): 1873–1879.

Moore KL & Agure AMR. Essential Clinical Anatomy. 3thed. Philadelphia: Lippincott Williams & Wilkins; 2007.

Nyberg DA, Jeffrey RB, Brant-Zawadzki M, Federle M, Dillon W. Computed tomography of cervical infections. J Comput Assist Tomogr. 1985; 9(2): 288– 96.

Obayashi N, Ariji Y, Goto M, Izumi M, Naitoh M, Kurita K, Shimozato K, Ariji E. Spread of odontogenic infection originating in the maxillary teeth:Computerized 137

tomographic assessment. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2004; 98(2): 223–231.

Ohshima A, Ariji Y, Gotoh M, Izumi M, Naitoh M, Kurita K, Shimozato K, and Ariji E. Computed tomographic anatomy of the mandibular first and second molars and their surrounding structures in the spread of odontogenic infection. Oral Radiology. 2009; 25(2): 99–107.

Osborn TM, Assael LA, Bell RB. Deep space neck infection: Principles of surgical management. Oral Maxillofacial Surg Clin N Am. 2008; 20(3): 353–365.

Parisi M, Mehdizadeh HM, Hunter JC, and Finch IJ. Evaluation of craniosynostosis with three-dimensional CT imaging. J. Comput. Assist. Tomogr.1989; 13(6): 1006–1012.

Peters ES, Fong B, Wormuth DW, and Sonis ST. Risk factors affecting hospital length of stay in patients with odontogenic maxillofacial infections. J Oral Maxillofacial Surgery. 1996; 54(12): 1386–1391.

Peterson LJ. Contemporary management of deep infections of the neck. J Oral Maxillofac Surg. 1993: 51(3): 226–231.

Pinto A, Scaglione M, Scuderi MG, Tortora G, Daniele S, Romano L. Infections of the neck leading to descending necrotizing mediastinitis: Role of multi-detector row computed tomography. European Journal of Radiology. 2008; 65(3): 389–394.

Rapidis AD, Dijkstra PU, Roodenburg JL, Rodrigo JP, Rinaldo A, Strojan P, Takes RP, Ferlito A. Trismus in patients with head and neck cancer. Etiopathogenesis, diagnosis and management. Clinical Otolaryngolgy. 2015.

Rega AJ, Aziz SR, Ziccardi VB. Microbiology and antibiotic sensitivities of head and neck space infections of odontogenic origin. J Oral Maxillofac Surg 2006;/64(9):/1377– 1380.

Reuben AD, Watt-Smith SR, Dobson D, Golding SJ. A comparative study of evaluation of radiographs, CT and 3D reformatted CT in facial trauma: what is the role of 3D? The British Journal of Radiology.2005; 78(927): 198–201.

138

Reynolds SC & Chow AW. Life-Threatening Infections of the peripharyngeal and deep fascial spaces of the head and neck. Infect Dis Clin North Am. 2007; 21(2): 557–576.

Richard HH, Michael JH, And Thomas I. An Epidemiologic and Anatomic Survey of Odon togenic Infections. J Oral Marillofac Surg. 1991; 49(9): 976–980.

Ridder GJ, Technau-Ihling K, Sander A, Boedeker CC. Spectrum and management of deep neck space infections: an 8-year experience of 234 cases. Otolaryngol Head Neck Surg. 2005; 133(5): 709–714.

Ring H, Stiassny F, Schima W, Toepker M, Czerny C, Schueller G , Asenbaum U, Furtner J, Hajdu S, Serles W, Weber M, Herold CJ. Intracranial Hematomas at a Glance: Advanced Visualization for Fast and Easy Detection. Radiology. 2013; 267(2): 522–530.

Rosen EJ. Deep neck spaces and infections. Grand Rounds Presentation, UTMB, Dept. of Otolaryngology. 2002.

Rosenthal M, Oreadi D, Kraus J, Bedi H, Stark PC, Shastri K. Comparison of Preoperative Computed Tomography and Surgical Findings in Maxillofacial Infections. J Oral Maxillofac. Surg. 2011; 69(6): 1651–1656.

Rosset A, Spadola L, Ratib O. OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images. J Digit Imaging. 2004; 17(3): 205–216.

Rosset A, Spadola L, Pysher L, and Ratib O. Informatics in radiology (infoRAD): navigating the fifth dimension: innovative interface for multidimensional multimodality image navigation. Radiographics. 2006; 26(1): 299–308.

Salam HA. (2008) An overview of odontogenic infections. When to refer? Smile: The Dental Magazine [homepage on the internet]. Available from: http://www.smiledentaljournal.com/index.php?option=com_content&view=article&id=1 91%3Aan-overview-of-odontogenicinfections&catid=934&Itemid=130

Sannomiya EK, Silva JV, Brito AA, Saez DM, Angelieri F, Dalben Gda S. Surgical planning for resection of an ameloblastoma and reconstruction of the mandible using a selective laser sintering 3D biomodel. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008; 106(1): 36–40. 139

Sato FR, Hajala FA, Freire Filho FW, Moreira RW, de Moraes M. Eight-Year Retrospective Study of Odontogenic Origin Infections in a Postgraduation Program on Oral and Maxillofacial Surgery. J Oral Maxillofac Surg. 2009; 67(5): 1092–1097.

Schuknecht B., Stergiou G., Graetz K. Masticator space abscess derived from odontogenic infection: imaging manifestation and pathways of extension depicted by CT and MR in 30 patients. Eur Radiol. 2008; 18(9): 1972–1979.

Shahbazian M, Jacobs R. Diagnostic value of 2D and 3D imaging in odontogenic maxillary sinusitis: a review of literature. Journal of Oral Rehabilitation. 2012; 39(4): 294–300.

Sichel JY, Attal P, Hocwald E, Eliashar R. Redefining Parapharyngeal Space Infections. Annals of Otology, Rhinology & Laryngology. 2006; 115(2): 117–123.

Smith JK, Armao DM, Specter BB, Castillo M. Danger space infection: infection of the neck leading to descending necrotizing mediastinitis. Emergency Radiology.1999; 6(3):129 132.

Smith JL, Hsu JM, Chang J. Predicting deep neck space abscess using computed tomography. Am J Otolaryngol. 2006; 27(4): 244–247.

Storoe W, Haug RH, Lillich TT. The changing face of odontogenic infections. J Oral Maxillofac Surg. 2001; 59(7): 739–48.

Sugimoto M, Yasuda H, Koda K, Suzuki M, Yamazaki M, Tezuka T, Kosugi C, Higuchi R, Watayo Y, Yagawa Y, Uemura S, Tsuchiya H, Azuma T. Image overlay navigation by markerless surface registration in gastrointestinal, hepatobiliary and pancreatic surgery. J Hepatobiliary Pancreat Sci. 2009; 17(5): 629–636.

Tatar I. OsiriX: Is it really a suitable software for 3D visualization of neuroanatomical structures acquired from DICOM images? Neuroanatomy. 2008; 7: 20–21.

Timothy Alberg. A Guide to OsiriX; 2010: 8-24. Available from: https://webblaburi.files.wordpress.com/2013/10/guide_to_osirix.pdf

140

Topazian RG, Goldberg MH. 1987. Odontogenic infections and deep fascial space infections of dental origin.In: Topazian RG, Goldberg MH, editors. Oral and maxillofacial infections (2nd edition) (P. 170). Philadelphia, NY: WB Saunders Company.

Uluibau IC, Jaunay T, Goss AN. Severe odontogenic infections. Aust Dent J. 2005; 50(4): 74–81.

Ungkanont K, Yellon RF, Weissman JL, Casselbrant ML, González-Valdepeña H, Bluestone CD. Head and neck space infections in infants and children. Otolaryngol Head Neck Surg. 1995;112(3): 375– 382.

Volonté F, Pugin F, Bucher P, Sugimoto M, Ratib O, Morel P. Augmented reality and image overlay navigation with OsiriX in laparoscopic and robotic surgery: not only a matter of fashion. J Hepatobiliary Pancreat Sci. 2011; 18(4): 506–509.

Wabik A, Hendrich BK., Nienartowicz J, Guziński M, Sąsiadek MJ. Odontogenic Inflammatory Processes of Head and Neck in Computed Tomography Examinations.Pol J Radiol. 2014; 79: 431– 438.

Wang J, Ahani A, Pogrel MA. A five-year retrospective study of odontogenic maxillofacial infections in a large urban public hospital. Int. J. Oral Maxillofac. Surg. 2005; 34(6): 646–649.

Wang LF, Tai CF, Kuo WR, Chien CY. Predisposing Factors of Complicated Deep Neck Infections: 12-Year Experience at a Single Institution. Journal of Otolaryngology-Head & Neck Surgery. 2010; 39(4): 335–341.

Wang TY. DA Nationwide Survey of Deaths From Oral and Maxillofacial Infections: The Taiwanese Experience. J Oral Maxillofac Surg. 1999; 57: 1297–l299.

Wang YC, Liu YC, Hsieh TC, Lee ST, Li ML. Aneurysmal subarachnoid hemorrhage diagnosis with computed tomographic angiography and OsiriX. Acta Neurochir. 2010; 152(2): 263–269.

Wills PI, Vernon JR. Complications of space infections of the head and neck. Laryngoscope. 1981; 91(7): 1129–1136. 141

Xu DM, van Klaveren RJ, de Bock GH, Leusveld A, Zhao Y, Wang Y, Vliegenthart R, de Koning HJ, Scholten ET, Verschakelen J, Prokop M, Oudkerk M. Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the Nelson trial. European Journal of Radiology. 2008; 68(2): 347–352.

Ylijoki S, Suuronen R, Jousimies-Somer H, Meurman JH., Lindqvist C. Differences Between Patients With or Without the Need for Intensive Care Due to Severe Odontogenic Infections. J Oral Maxillofac Surg. 2001; 59(8): 867–872.

Yonetsu K, Izumi M, Nakamura T. Deep Facial Infections of Odontogenic Origin:CT Assessment of Pathways of Space Involvement. AJNR Am J Neuroradiol. 1998; 19(1):123–128.

Zhang C, Tang Y, Zheng M, Yang J, Zhu G, Zhou H, Zhang Z, Liang X. Maxillofacial space infection experience in West China: a retrospective study of 212 cases. International Journal of Infectious Diseases. 2010; 14(5): 414–417.

142

Appendix A: Raw Data – Demographic

Pt_ID age_ gender race Caucasian African_A 3 30 1 AFRICAN AMERICAN/BLK 0 1 4 49 1 Caucasian 1 0 5 50 0 Caucasian 1 0 6 54 0 AFRICAN AMERICAN/BLK 0 1 8 33 0 AFRICAN AMERICAN/BLK 0 1 9 52 1 Caucasian 1 0 10 45 1 Caucasian 1 0 12 27 0 Caucasian 1 0 13 49 0 Caucasian 1 0 14 35 1 Caucasian 1 0 15 59 0 AFRICAN AMERICAN/BLK 0 1 17 70 0 Caucasian 1 0 18 47 0 Caucasian 1 0 19 42 1 AFRICAN AMERICAN/BLK 0 1 21 62 0 AFRICAN AMERICAN/BLK 0 1 22 52 0 Caucasian 1 0 24 50 0 Caucasian 1 0 28 27 1 Caucasian 1 0 33 35 0 Caucasian 1 0 36 40 1 Caucasian 1 0 37 60 1 Caucasian 1 0 38 31 1 AFRICAN AMERICAN/BLK 0 1 40 38 1 Caucasian 1 0 41 39 0 Caucasian 1 0 42 41 0 Caucasian 1 0 45 28 1 Caucasian 1 0 46 32 0 Caucasian 1 0 49 58 1 AFRICAN AMERICAN/BLK 0 1 50 47 0 Caucasian 1 0 51 47 1 Caucasian 1 0 52 67 0 Caucasian 1 0 53 42 1 Caucasian 1 0 55 64 1 Caucasian 1 0 56 25 1 Caucasian 1 0

143

Continuous: Demographic

57 29 1 AFRICAN AMERICAN/BLK 0 1 61 31 0 Caucasian 1 0 63 24 1 AFRICAN AMERICAN/BLK 0 1 64 36 0 AFRICAN AMERICAN/BLK 0 1 66 53 0 Caucasian 1 0 68 38 0 Caucasian 1 0 69 41 0 AFRICAN AMERICAN/BLK 0 1 71 33 1 AFRICAN AMERICAN/BLK 0 1 75 38 0 Caucasian 1 0 79 42 0 AFRICAN AMERICAN/BLK 0 1 81 49 1 Caucasian 1 0 83 23 0 Caucasian 1 0 86 27 1 AFRICAN AMERICAN/BLK 0 1 87 27 0 Caucasian 1 0 88 45 1 Caucasian 1 0 89 28 1 Caucasian 1 0 90 25 1 Caucasian 1 0 91 33 1 AFRICAN AMERICAN/BLK 0 1 92 33 1 AFRICAN AMERICAN/BLK 0 1 94 49 0 Caucasian 1 0 95 29 1 AFRICAN AMERICAN/BLK 0 1 96 29 1 Caucasian 1 0 97 32 1 Caucasian 1 0 99 24 0 Caucasian 1 0 100 26 0 Caucasian 1 0 103 64 0 Caucasian 1 0 107 29 0 Caucasian 1 0 110 21 0 Caucasian 1 0 112 51 1 Caucasian 1 0 114 62 0 Caucasian 1 0 116 28 0 AFRICAN AMERICAN/BLK 0 1 117 25 1 Caucasian 1 0 118 52 1 Caucasian 1 0 120 45 0 AFRICAN AMERICAN/BLK 0 1 122 34 1 Caucasian 1 0 144

Continuous: Demographic Data

123 32 0 AFRICAN AMERICAN/BLK 0 1 127 47 0 Caucasian 1 0 129 31 1 Caucasian 1 0 130 59 0 Caucasian 1 0 131 44 0 Caucasian 1 0 132 23 1 Caucasian 1 0 133 53 0 Caucasian 1 0 137 24 1 AFRICAN AMERICAN/BLK 0 1 139 36 1 Caucasian 1 0 141 51 1 Caucasian 1 0 144 35 1 Caucasian 1 0 145 18 1 AFRICAN AMERICAN/BLK 0 1 150 36 1 Caucasian 1 0 154 31 1 Caucasian 1 0 155 32 1 AFRICAN AMERICAN/BLK 0 1 157 33 0 Caucasian 1 0 158 25 1 Caucasian 1 0 159 65 0 Caucasian 1 0 160 31 0 Caucasian 1 0 163 44 0 AFRICAN AMERICAN/BLK 0 1 164 25 1 Caucasian 1 0 165 22 1 Caucasian 1 0 166 35 1 Caucasian 1 0 167 33 0 AFRICAN AMERICAN/BLK 0 1 168 33 0 Caucasian 1 0 170 58 0 Caucasian 1 0 172 55 0 Caucasian 1 0 173 24 1 Caucasian 1 0 175 53 1 AFRICAN AMERICAN/BLK 0 1 176 32 1 Caucasian 1 0 180 27 0 Caucasian 1 0 185 34 0 Caucasian 1 0 187 32 0 Caucasian 1 0 188 56 1 Caucasian 1 0

145

Raw Data: Comorbidity

Pt_ID diabetes_yn hypertension_ynRenal_disease cancer_yn Etiology 3 0 0 0 0 Dental caries 4 0 0 0 0 Dental caries 5 0 0 0 0 Dental caries 6 1 1 0 0 Dental caries 8 0 0 0 0 Dental caries 9 0 1 0 0 Dental caries 10 0 0 0 0 Dental caries 12 0 0 0 0 Dental caries 13 0 0 0 0 Dental caries 14 1 1 1 0 Dental caries 15 0 1 0 0 Dental caries 17 0 1 0 0 Dental caries 18 0 0 0 0 Dental caries 19 0 0 0 0 Mandibular Fracture 21 1 1 0 0 Dental caries 22 0 0 0 0 Dental caries 24 0 0 0 0 Dental caries 28 1 0 0 0 Dental caries 33 0 0 0 0 Dental caries 36 0 0 0 0 Glandular infection. 37 0 0 0 0 Failure Dental Implant 38 0 0 0 0 Dental caries 40 0 0 0 0 Dental caries 41 0 1 0 0 Dental caries 42 0 0 0 0 Dental caries 45 0 0 0 0 Dental caries 46 0 0 0 0 Periodontal disease 49 1 1 1 0 Dental caries 50 0 0 0 0 Dental caries 51 1 0 0 0 Dental caries 52 0 1 0 1 Dental caries 53 0 0 0 0 Dental caries 55 1 1 0 1 Dental caries 56 0 0 0 0 Dental caries 146

57 0 0 0 0 Dental caries 61 0 0 0 0 Dental caries 63 0 0 0 0 Dental caries 64 0 0 0 0 Dental caries 66 0 0 0 1 Dental caries 68 0 0 0 0 Dental caries 69 0 0 0 0 Dental caries 71 0 0 0 0 Mandibular Fracture 75 0 1 0 0 Periodontal disease 79 0 0 0 0 Dental caries 81 0 0 0 0 Dental caries 83 0 0 0 0 Dental caries 86 0 0 0 0 Dental caries 87 0 0 0 0 Dental caries 88 0 1 0 0 Dental caries 89 0 0 0 0 Mandibular Fracture 90 0 0 0 0 Dental caries 91 1 0 0 0 Dental caries 92 0 0 0 0 Dental caries 94 0 0 0 0 Dental caries 95 0 0 0 0 Dental caries 96 0 0 0 0 Dental caries 97 0 0 0 0 Dental caries 99 0 0 0 0 Periodontal disease 100 0 0 0 0 Dental caries 103 0 1 0 1 Unknown 107 0 0 0 0 Fracture of Oribital rim 110 0 0 0 0 Dental caries 112 0 0 0 0 Unknown 114 1 1 0 1 Periodontal disease 116 0 0 0 0 117 0 0 0 0 Dental caries 118 0 0 0 0 Dental caries 120 0 0 0 0 Periodontal disease 122 0 0 0 0 147

123 0 0 0 0 Dental caries 127 1 0 0 0 Dental caries 129 0 0 0 0 Dental caries 130 1 1 0 0 Dental caries 131 0 0 0 0 Dental caries 132 0 0 0 0 Dental caries 133 0 0 0 0 Dental caries 137 0 0 0 0 Mandible fracture 139 0 1 1 0 Dental caries 141 0 1 0 0 Dental caries 144 0 0 0 0 Dental caries 145 0 0 0 0 Dental caries 150 0 0 0 0 Pericoronitis related to tooth #32 154 0 1 0 0 Dental caries 155 0 0 0 0 Dental caries 157 0 0 0 0 Dental caries 158 0 0 0 0 Dental caries 159 0 1 0 0 Dental caries 160 0 0 0 0 Periodontal disease 163 0 0 0 0 Dental caries 164 0 0 0 0 Dental caries 165 0 0 0 0 Dental caries 166 0 0 0 0 Dental caries 167 0 0 0 0 Dental caries 168 0 0 0 0 Dental caries 170 0 0 0 0 Dental caries 172 0 0 0 0 Dental caries 173 0 0 0 0 Dental caries 175 0 0 0 0 Dental caries 176 0 0 0 0 Dental caries 180 0 0 0 0 Dental caries 185 0 0 0 0 Dental caries 187 0 0 0 0 Dental caries 188 0 0 0 0 Dental caries 148

Raw Data: Facial Spaces

149

150

151

Continuous Facial Spaces

152

153

154

Continuous of Facial Spaces and Complications

155

156

157

3D Comparison between affected and non-affected muscles

158

159

160

3D % of affected muscles, volume and pixels of ROIs

161

162

163

Appendix B: Descriptive Statistics Frequencies

Statistics

age_ gender race N Valid 103 103 300

Missing 197 197 0 Mean 39.33 .50 Median 35.00 1.00 Mode 33 1 Std. Deviation 12.569 .502 Variance 157.988 .252 Range 52 1 Minimum 18 0 Maximum 70 1 Percentiles 25 29.00 .00

50 35.00 1.00 75 49.00 1.00

Frequency Table

age_

Cumulative Frequency Percent Valid Percent Percent Valid 18 1 .3 1.0 1.0

21 1 .3 1.0 1.9 22 1 .3 1.0 2.9 23 2 .7 1.9 4.9 24 4 1.3 3.9 8.7 164

25 5 1.7 4.9 13.6 26 1 .3 1.0 14.6 27 5 1.7 4.9 19.4 28 3 1.0 2.9 22.3 29 4 1.3 3.9 26.2 30 1 .3 1.0 27.2

31 5 1.7 4.9 32.0 32 6 2.0 5.8 37.9 33 7 2.3 6.8 44.7 34 2 .7 1.9 46.6 35 4 1.3 3.9 50.5 36 3 1.0 2.9 53.4 38 3 1.0 2.9 56.3 39 1 .3 1.0 57.3 40 1 .3 1.0 58.3

41 2 .7 1.9 60.2 42 3 1.0 2.9 63.1 44 2 .7 1.9 65.0 45 3 1.0 2.9 68.0 47 4 1.3 3.9 71.8 49 4 1.3 3.9 75.7 50 2 .7 1.9 77.7 51 2 .7 1.9 79.6 52 3 1.0 2.9 82.5

53 3 1.0 2.9 85.4 54 1 .3 1.0 86.4 55 1 .3 1.0 87.4 56 1 .3 1.0 88.3 165

58 2 .7 1.9 90.3 59 2 .7 1.9 92.2 60 1 .3 1.0 93.2 62 2 .7 1.9 95.1 64 2 .7 1.9 97.1 65 1 .3 1.0 98.1

67 1 .3 1.0 99.0 70 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

gender

Cumulative Frequency Percent Valid Percent Percent Valid Female 51 17.0 49.5 49.5 Male 52 17.3 50.5 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

166

race

Cumulative Frequency Percent Valid Percent Percent Valid 197 65.7 65.7 65.7 AFRICAN AMERICAN/BLK 27 9.0 9.0 74.7 WHITE 76 25.3 25.3 100.0

Total 300 100.0 100.0

Descriptives Descriptive Statistics

N Minimum Maximum Mean Std. Deviation age_ 103 18 70 39.33 12.569 gender 103 0 1 .50 .502 Valid N (listwise) 103

167

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation diabetes_yn 103 0 1 .11 .310 hypertension_yn 103 0 1 .18 .390 Renal_disease 103 0 1 .03 .169 cancer_yn 103 0 1 .05 .216 Valid N (listwise) 103

Frequencies

Statistics

Air_way_obstruc enlargement_su enlargement_parot tion bmandibular id Trismus

N Valid 103 103 103 103 Missing 197 197 197 197 Mean .19 .28 .06 .07 Median .00 .00 .00 .00 Mode 0 0 0 0 Std. Deviation .397 .452 .235 .253 Variance .158 .204 .055 .064 Range 1 1 1 1 Minimum 0 0 0 0 Maximum 1 1 1 1 Percentiles 25 .00 .00 .00 .00 50 .00 .00 .00 .00 75 .00 1.00 .00 .00

168

Frequency Table

Air_way_obstruction

Cumulative Frequency Percent Valid Percent Percent

Valid No 83 27.7 80.6 80.6 Yes 20 6.7 19.4 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

enlargement_submandibular

Cumulative Frequency Percent Valid Percent Percent Valid No 74 24.7 71.8 71.8

Yes 29 9.7 28.2 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

enlargement_parotid

Cumulative Frequency Percent Valid Percent Percent Valid No 97 32.3 94.2 94.2

Yes 6 2.0 5.8 100.0 Total 103 34.3 100.0 Missing System 197 65.7

169

Total 300 100.0

Trismus

Cumulative Frequency Percent Valid Percent Percent Valid No 96 32.0 93.2 93.2 Yes 7 2.3 6.8 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Frequency Table Buccal_space

Cumulative Frequency Percent Valid Percent Percent Valid No 85 28.3 82.5 82.5 Yes 18 6.0 17.5 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Retromolar_trigone_region

Cumulative Frequency Percent Valid Percent Percent Valid No 94 31.3 91.3 91.3 Yes 9 3.0 8.7 100.0

170

Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Submental_space

Cumulative Frequency Percent Valid Percent Percent Valid No 58 19.3 56.3 56.3 Yes 45 15.0 43.7 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Sublingual_space

Cumulative Frequency Percent Valid Percent Percent Valid No 51 17.0 49.5 49.5 Yes 52 17.3 50.5 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

171

Parotid_space

Cumulative Frequency Percent Valid Percent Percent Valid No 95 31.7 92.2 92.2

Yes 8 2.7 7.8 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Masticator_space_

Cumulative Frequency Percent Valid Percent Percent

Valid No 57 19.0 55.3 55.3 Yes 46 15.3 44.7 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Parapharyngeal_space

Cumulative Frequency Percent Valid Percent Percent

Valid No 70 23.3 68.0 68.0 Yes 33 11.0 32.0 100.0 Total 103 34.3 100.0

172

Missing System 197 65.7 Total 300 100.0

Carotid_space

Cumulative Frequency Percent Valid Percent Percent Valid No 94 31.3 91.3 91.3 Yes 9 3.0 8.7 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Retropharyngeal_space

Cumulative Frequency Percent Valid Percent Percent Valid No 97 32.3 94.2 94.2 Yes 6 2.0 5.8 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Danger_space_

Cumulative Frequency Percent Valid Percent Percent

Valid No 102 34.0 99.0 99.0 Yes 1 .3 1.0 100.0 Total 103 34.3 100.0

173

Missing System 197 65.7 Total 300 100.0

Mediastinum

Cumulative Frequency Percent Valid Percent Percent Valid No 96 32.0 93.2 93.2 Yes 7 2.3 6.8 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Frequencies Statistics Multiple_Space_infection N Valid 103 Missing 197 Mean .9029 Median 1.0000 Mode 1.00 Std. Deviation .29752 Variance .089 Range 1.00 Minimum .00 Maximum 1.00 Sum 93.00 Percentiles 25 1.0000

174

50 1.0000 75 1.0000

Multiple_Space_infection

Cumulative Frequency Percent Valid Percent Percent Valid No 10 3.3 9.7 9.7 Yes 93 31.0 90.3 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

Descriptives

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Time_Betw_adm_and_secon 17.39999999996 208.9333333333 101.9454545454 65.50963122191 11 d_surg_hours 5 49 8312 6180 los_days 103 1.3 45.2 5.606 4.9494 Second_los_days 11 2.2 12.0 8.081 3.2597 Valid N (listwise) 11

Frequencies Statistics

175

Time_Betw_adm Time_Betw_adm _and_Firs_Surg _and_second_su Second_los_day _hours los_days rg_hours s N Valid 103 103 11 11 Missing 197 197 289 289 Mean 101.9454545454 27.90 5.606 8.081 8312 Median 91.26666666660 14.30 4.061 7.081 458 Mode 17.39999999996 9 1.3a 2.2a 5a Std. Deviation 65.50963122191 83.623 4.9494 3.2597 6180 Variance 6992.753 24.496 4291.512 10.626 Range 191.5333333333 838 43.9 9.8 84 Minimum 17.39999999996 0 1.3 2.2 5 Maximum 208.9333333333 838 45.2 12.0 49 Percentiles 25 25.64999999996 7.00 3.422 6.000 508 50 91.26666666660 14.30 4.061 7.081 458 75 143.1833333334 23.33 6.022 11.972 6527 a. Multiple modes exist. The smallest value is shown

176

Frequencies Statistics

Time_Betw_adm Time_Betw_adm _and_Firs_Surg_ _and_second_su Second_los_day los_days hours rg_hours s N Valid 103 103 11 11

Missing 197 197 289 289 Mean 101.9454545454 5.606 27.90 8.081 8312 Median 91.26666666660 4.061 14.30 7.081 458 Mode 17.39999999996 1.3a 9 2.2a 5a Std. Deviation 65.50963122191 4.9494 83.623 3.2597 6180 Variance 24.496 6992.753 4291.512 10.626 Range 191.5333333333 43.9 838 9.8 84 Minimum 17.39999999996 1.3 0 2.2 5 Maximum 208.9333333333 45.2 838 12.0 49 Sum 1121.400000000 577.4 2874 88.9 314 Percentiles 25 25.64999999996 3.422 7.00 6.000 508 50 91.26666666660 4.061 14.30 7.081 458 75 143.1833333334 6.022 23.33 11.972 6527 a. Multiple modes exist. The smallest value is shown 177

Frequency Table

los_days

Cumulative Frequency Percent Valid Percent Percent Valid 1.3 1 .3 1.0 1.0 1.8 1 .3 1.0 1.9 2.0 1 .3 1.0 2.9 2.1 1 .3 1.0 3.9 2.1 1 .3 1.0 4.9 2.2 1 .3 1.0 5.8 2.4 1 .3 1.0 6.8 2.5 1 .3 1.0 7.8

2.7 1 .3 1.0 8.7 2.8 1 .3 1.0 9.7 2.9 1 .3 1.0 10.7 3.0 1 .3 1.0 11.7 3.0 1 .3 1.0 12.6 3.0 1 .3 1.0 13.6 3.0 1 .3 1.0 14.6 3.0 1 .3 1.0 15.5 3.0 1 .3 1.0 16.5 3.1 1 .3 1.0 17.5

3.1 1 .3 1.0 18.4 3.1 1 .3 1.0 19.4 3.2 1 .3 1.0 20.4

178

3.2 1 .3 1.0 21.4 3.2 1 .3 1.0 22.3 3.3 1 .3 1.0 23.3 3.3 1 .3 1.0 24.3 3.4 1 .3 1.0 25.2 3.5 1 .3 1.0 26.2

3.5 1 .3 1.0 27.2 3.6 1 .3 1.0 28.2 3.6 1 .3 1.0 29.1 3.6 1 .3 1.0 30.1 3.7 1 .3 1.0 31.1 3.7 1 .3 1.0 32.0 3.7 1 .3 1.0 33.0 3.8 1 .3 1.0 34.0 3.8 1 .3 1.0 35.0

3.8 1 .3 1.0 35.9 3.8 1 .3 1.0 36.9 3.8 1 .3 1.0 37.9 3.8 1 .3 1.0 38.8 3.9 1 .3 1.0 39.8 3.9 1 .3 1.0 40.8 3.9 1 .3 1.0 41.7 3.9 1 .3 1.0 42.7 3.9 1 .3 1.0 43.7

3.9 1 .3 1.0 44.7 4.0 1 .3 1.0 45.6 4.0 1 .3 1.0 46.6 4.0 1 .3 1.0 47.6 179

4.0 1 .3 1.0 48.5 4.0 1 .3 1.0 49.5 4.1 1 .3 1.0 50.5 4.1 1 .3 1.0 51.5 4.1 1 .3 1.0 52.4 4.2 1 .3 1.0 53.4

4.2 1 .3 1.0 54.4 4.2 1 .3 1.0 55.3 4.3 1 .3 1.0 56.3 4.4 1 .3 1.0 57.3 4.5 1 .3 1.0 58.3 4.6 1 .3 1.0 59.2 4.6 1 .3 1.0 60.2 4.6 1 .3 1.0 61.2 4.7 1 .3 1.0 62.1

4.7 1 .3 1.0 63.1 4.9 1 .3 1.0 64.1 4.9 1 .3 1.0 65.0 4.9 1 .3 1.0 66.0 5.0 1 .3 1.0 67.0 5.1 1 .3 1.0 68.0 5.1 1 .3 1.0 68.9 5.2 1 .3 1.0 69.9 5.3 1 .3 1.0 70.9

5.4 1 .3 1.0 71.8 5.4 1 .3 1.0 72.8 5.5 1 .3 1.0 73.8 6.0 1 .3 1.0 74.8 180

6.0 1 .3 1.0 75.7 6.1 1 .3 1.0 76.7 6.3 1 .3 1.0 77.7 6.8 1 .3 1.0 78.6 6.9 1 .3 1.0 79.6 7.1 1 .3 1.0 80.6

7.1 1 .3 1.0 81.6 7.2 1 .3 1.0 82.5 7.8 1 .3 1.0 83.5 7.9 1 .3 1.0 84.5 8.3 1 .3 1.0 85.4 8.6 1 .3 1.0 86.4 8.7 1 .3 1.0 87.4 9.4 1 .3 1.0 88.3 10.1 1 .3 1.0 89.3

11.1 1 .3 1.0 90.3 11.5 1 .3 1.0 91.3 11.7 1 .3 1.0 92.2 11.8 1 .3 1.0 93.2 12.0 1 .3 1.0 94.2 12.0 1 .3 1.0 95.1 12.0 1 .3 1.0 96.1 14.2 1 .3 1.0 97.1 14.7 1 .3 1.0 98.1

16.0 1 .3 1.0 99.0 45.2 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7

181

Total 300 100.0

Time_Betw_adm_and_Firs_Surg_hours

Cumulative Frequency Percent Valid Percent Percent Valid 0 1 .3 1.0 1.0 1 1 .3 1.0 1.9 1 1 .3 1.0 2.9 2 1 .3 1.0 3.9 2 1 .3 1.0 4.9 3 1 .3 1.0 5.8 3 1 .3 1.0 6.8 3 1 .3 1.0 7.8

3 1 .3 1.0 8.7 3 1 .3 1.0 9.7 3 1 .3 1.0 10.7 4 1 .3 1.0 11.7 4 1 .3 1.0 12.6 4 1 .3 1.0 13.6 4 1 .3 1.0 14.6

5 1 .3 1.0 15.5 5 1 .3 1.0 16.5

5 1 .3 1.0 17.5 5 1 .3 1.0 18.4 5 1 .3 1.0 19.4 6 1 .3 1.0 20.4

182

6 1 .3 1.0 21.4 6 1 .3 1.0 22.3 6 1 .3 1.0 23.3 7 1 .3 1.0 24.3 7 1 .3 1.0 25.2 7 1 .3 1.0 26.2

7 1 .3 1.0 27.2 7 1 .3 1.0 28.2 7 1 .3 1.0 29.1 8 1 .3 1.0 30.1 8 1 .3 1.0 31.1 9 2 .7 1.9 33.0 9 1 .3 1.0 34.0 9 1 .3 1.0 35.0 9 1 .3 1.0 35.9

9 1 .3 1.0 36.9 9 1 .3 1.0 37.9 9 1 .3 1.0 38.8 9 1 .3 1.0 39.8 10 1 .3 1.0 40.8 10 1 .3 1.0 41.7 11 1 .3 1.0 42.7 11 1 .3 1.0 43.7 12 1 .3 1.0 44.7

12 1 .3 1.0 45.6 12 1 .3 1.0 46.6 14 1 .3 1.0 47.6 14 1 .3 1.0 48.5 183

14 1 .3 1.0 49.5 14 1 .3 1.0 50.5 14 1 .3 1.0 51.5 15 1 .3 1.0 52.4 15 1 .3 1.0 53.4 16 1 .3 1.0 54.4

16 1 .3 1.0 55.3 17 1 .3 1.0 56.3 17 1 .3 1.0 57.3 18 1 .3 1.0 58.3 18 1 .3 1.0 59.2 18 1 .3 1.0 60.2 20 1 .3 1.0 61.2 20 1 .3 1.0 62.1 20 1 .3 1.0 63.1

20 1 .3 1.0 64.1 20 1 .3 1.0 65.0 20 1 .3 1.0 66.0 22 1 .3 1.0 67.0 22 1 .3 1.0 68.0 22 1 .3 1.0 68.9 22 1 .3 1.0 69.9 22 1 .3 1.0 70.9 23 1 .3 1.0 71.8

23 1 .3 1.0 72.8 23 1 .3 1.0 73.8 23 1 .3 1.0 74.8 23 1 .3 1.0 75.7 184

24 1 .3 1.0 76.7 25 1 .3 1.0 77.7 25 1 .3 1.0 78.6 25 1 .3 1.0 79.6 26 1 .3 1.0 80.6 27 1 .3 1.0 81.6

27 1 .3 1.0 82.5 27 1 .3 1.0 83.5 29 1 .3 1.0 84.5 30 1 .3 1.0 85.4 30 1 .3 1.0 86.4 40 1 .3 1.0 87.4 43 1 .3 1.0 88.3 44 1 .3 1.0 89.3 45 1 .3 1.0 90.3

45 1 .3 1.0 91.3 51 1 .3 1.0 92.2 64 1 .3 1.0 93.2 67 1 .3 1.0 94.2 69 1 .3 1.0 95.1 81 1 .3 1.0 96.1 86 1 .3 1.0 97.1 87 1 .3 1.0 98.1 150 1 .3 1.0 99.0

838 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

185

Time_Betw_adm_and_second_surg_hours

Cumulative Frequency Percent Valid Percent Percent Valid 17.399999999965 1 .3 9.1 9.1

20.950000000012 1 .3 9.1 18.2 25.649999999965 1 .3 9.1 27.3 75.816666666709 1 .3 9.1 36.4 87.466666666791 1 .3 9.1 45.5 91.266666666605 1 .3 9.1 54.5 125.733333333337 1 .3 9.1 63.6 136.683333333407 1 .3 9.1 72.7 143.183333333465 1 .3 9.1 81.8 188.316666666709 1 .3 9.1 90.9 208.933333333349 1 .3 9.1 100.0

Total 11 3.7 100.0 Missing System 289 96.3 Total 300 100.0

Second_los_days

Cumulative Frequency Percent Valid Percent Percent Valid 2.2 1 .3 9.1 9.1 5.7 1 .3 9.1 18.2

6.0 1 .3 9.1 27.3 6.0 1 .3 9.1 36.4 6.9 1 .3 9.1 45.5 7.1 1 .3 9.1 54.5 186

7.9 1 .3 9.1 63.6 11.1 1 .3 9.1 72.7 12.0 1 .3 9.1 81.8 12.0 1 .3 9.1 90.9 12.0 1 .3 9.1 100.0 Total 11 3.7 100.0 Missing System 289 96.3 Total 300 100.0

Descriptives

Descriptive Statistics

N Minimum Maximum Sum Mean Std. Deviation IM3D 103 8.5225 61.5125 2810.8635 27.289937 9.7009244 M3D 103 8.4479 43.0358 2148.2305 20.856607 7.0653019 Valid N (listwise) 103

Descriptive Statistics

N Range Minimum Maximum Sum Mean Std. Deviation Variance IMP3D 103 37.9863 2.8957 40.8820 1335.3295 12.964364 6.0540780 36.652 MP_3D 103 15.8393 4.5614 20.4007 990.5643 9.617129 3.2226535 10.385 ILP3D 103 11.3377 2.0699 13.4076 679.9715 6.601665 2.0132736 4.053 LP3D 103 7.9632 2.8644 10.8276 667.0090 6.475816 1.7930358 3.215 Valid N (listwise) 103

Descriptive Statistics 187

N Range Minimum Maximum Sum Mean Std. Deviation Variance

ROI_1 79 48.2210 .5208 48.7418 814.1872 10.306167 9.7486950 95.037 ROI_2 4 17.9842 .6856 18.6698 24.1602 6.040050 8.4880529 72.047 Total_ROIs 79 48.2210 .5208 48.7418 838.3474 10.611992 9.8743604 97.503

Pixel_ROI_1 .11562179972 79 .534983185 .001855023 .536838208 9.086325733 .11501678143 .013 7

Pixel_ROI_2 .19240356454 4 .434873543 .003129678 .438003221 .661603877 .16540096925 .037 0

Total_Pixels .13119598948 79 .651092847 .001855023 .652947870 9.747929610 .12339151405 .017 3 Valid N (listwise) 4

Frequencies Statistics

IM3D M3D

N Valid 103 103 Missing 197 197 Mean 27.289937 20.856607 Median 25.476500 19.514500 Mode 17.6995 31.1675 Std. Deviation 9.7009244 7.0653019 Variance 94.108 49.918 Range 52.9900 34.5879 Minimum 8.5225 8.4479 Maximum 61.5125 43.0358 Percentiles 25 21.317800 16.400400

50 25.476500 19.514500 75 32.204300 25.037700

188

IM3D

Cumulative Frequency Percent Valid Percent Percent Valid 8.5225 1 .3 1.0 1.0

12.1142 1 .3 1.0 1.9 13.6145 1 .3 1.0 2.9 14.5382 1 .3 1.0 3.9 14.6398 1 .3 1.0 4.9 15.3555 1 .3 1.0 5.8 15.4441 1 .3 1.0 6.8 15.4691 1 .3 1.0 7.8 15.4904 1 .3 1.0 8.7 16.3805 1 .3 1.0 9.7 16.5070 1 .3 1.0 10.7

16.5139 1 .3 1.0 11.7 16.8659 1 .3 1.0 12.6 17.1132 1 .3 1.0 13.6 17.4356 1 .3 1.0 14.6 17.6995 2 .7 1.9 16.5 18.4555 1 .3 1.0 17.5 18.5693 1 .3 1.0 18.4 18.8605 1 .3 1.0 19.4 19.1119 1 .3 1.0 20.4

19.3861 1 .3 1.0 21.4 19.5122 1 .3 1.0 22.3 19.9575 1 .3 1.0 23.3 21.2252 1 .3 1.0 24.3 189

21.3178 1 .3 1.0 25.2 21.5604 1 .3 1.0 26.2 21.5612 1 .3 1.0 27.2 21.7122 1 .3 1.0 28.2 21.7147 1 .3 1.0 29.1 21.7282 1 .3 1.0 30.1

21.7809 1 .3 1.0 31.1 21.8981 1 .3 1.0 32.0 22.1646 1 .3 1.0 33.0 22.2105 1 .3 1.0 34.0 22.2288 1 .3 1.0 35.0 22.2904 1 .3 1.0 35.9 22.5990 1 .3 1.0 36.9 22.6369 1 .3 1.0 37.9 22.7412 1 .3 1.0 38.8

22.8182 1 .3 1.0 39.8 22.8892 1 .3 1.0 40.8 22.9663 1 .3 1.0 41.7 23.4486 1 .3 1.0 42.7 23.4824 1 .3 1.0 43.7 23.4887 1 .3 1.0 44.7 24.1630 1 .3 1.0 45.6 24.5244 1 .3 1.0 46.6 24.8557 1 .3 1.0 47.6

24.9675 1 .3 1.0 48.5 25.3095 1 .3 1.0 49.5 25.4765 1 .3 1.0 50.5 25.4940 1 .3 1.0 51.5 190

25.6064 1 .3 1.0 52.4 25.8763 1 .3 1.0 53.4 25.9132 1 .3 1.0 54.4 26.5027 1 .3 1.0 55.3 26.6434 1 .3 1.0 56.3 26.7764 1 .3 1.0 57.3

26.9412 1 .3 1.0 58.3 27.0867 1 .3 1.0 59.2 27.8308 1 .3 1.0 60.2 27.8543 1 .3 1.0 61.2 28.6778 1 .3 1.0 62.1 28.8590 1 .3 1.0 63.1 29.0576 1 .3 1.0 64.1 29.1667 1 .3 1.0 65.0 29.6855 1 .3 1.0 66.0

30.6695 1 .3 1.0 67.0 30.9034 1 .3 1.0 68.0 31.1187 1 .3 1.0 68.9 31.2435 1 .3 1.0 69.9 31.3099 1 .3 1.0 70.9 31.3181 1 .3 1.0 71.8 31.4101 1 .3 1.0 72.8 31.5512 1 .3 1.0 73.8 31.6721 1 .3 1.0 74.8

32.2043 1 .3 1.0 75.7 32.3454 1 .3 1.0 76.7 32.5709 1 .3 1.0 77.7 33.6919 1 .3 1.0 78.6 191

35.0722 1 .3 1.0 79.6 35.6417 1 .3 1.0 80.6 35.7483 1 .3 1.0 81.6 36.0268 1 .3 1.0 82.5 36.3415 1 .3 1.0 83.5 36.7675 1 .3 1.0 84.5

36.7838 1 .3 1.0 85.4 37.5202 1 .3 1.0 86.4 37.9696 1 .3 1.0 87.4 41.1250 1 .3 1.0 88.3 41.2245 1 .3 1.0 89.3 41.5742 1 .3 1.0 90.3 42.1402 1 .3 1.0 91.3 42.2177 1 .3 1.0 92.2 42.9800 1 .3 1.0 93.2

43.9189 1 .3 1.0 94.2 44.3069 1 .3 1.0 95.1 45.0371 1 .3 1.0 96.1 47.5057 1 .3 1.0 97.1 50.6575 1 .3 1.0 98.1 53.7942 1 .3 1.0 99.0 61.5125 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

3DM

192

Cumulative Frequency Percent Valid Percent Percent Valid 8.4479 1 .3 1.0 1.0 9.6198 1 .3 1.0 1.9 9.8754 1 .3 1.0 2.9 10.2474 1 .3 1.0 3.9

10.3270 1 .3 1.0 4.9 10.3629 1 .3 1.0 5.8 10.9118 1 .3 1.0 6.8 11.3409 1 .3 1.0 7.8 11.6633 1 .3 1.0 8.7 11.8630 1 .3 1.0 9.7 12.5135 1 .3 1.0 10.7 12.7193 1 .3 1.0 11.7 12.7298 1 .3 1.0 12.6

12.8395 1 .3 1.0 13.6 12.9203 1 .3 1.0 14.6 13.5322 1 .3 1.0 15.5 14.0281 1 .3 1.0 16.5 14.3538 1 .3 1.0 17.5 14.4987 1 .3 1.0 18.4 14.5216 1 .3 1.0 19.4 14.8075 1 .3 1.0 20.4 15.0923 1 .3 1.0 21.4

15.4672 1 .3 1.0 22.3 15.9924 1 .3 1.0 23.3 16.3593 1 .3 1.0 24.3 16.4004 1 .3 1.0 25.2

193

16.5068 1 .3 1.0 26.2 16.5312 1 .3 1.0 27.2 16.6674 1 .3 1.0 28.2 17.0422 1 .3 1.0 29.1 17.0779 1 .3 1.0 30.1 17.0991 1 .3 1.0 31.1

17.1272 1 .3 1.0 32.0 17.3038 1 .3 1.0 33.0 17.3389 1 .3 1.0 34.0 17.3910 1 .3 1.0 35.0 17.8751 1 .3 1.0 35.9 17.9216 1 .3 1.0 36.9 17.9250 1 .3 1.0 37.9 18.1819 1 .3 1.0 38.8 18.2207 1 .3 1.0 39.8

18.3916 1 .3 1.0 40.8 18.4607 1 .3 1.0 41.7 18.4700 1 .3 1.0 42.7 18.6391 1 .3 1.0 43.7 18.8247 1 .3 1.0 44.7 18.8664 1 .3 1.0 45.6 19.1319 1 .3 1.0 46.6 19.3722 1 .3 1.0 47.6 19.4548 1 .3 1.0 48.5

19.5108 1 .3 1.0 49.5 19.5145 1 .3 1.0 50.5 19.6364 1 .3 1.0 51.5 19.6407 1 .3 1.0 52.4 194

19.9960 1 .3 1.0 53.4 20.1123 1 .3 1.0 54.4 20.1290 1 .3 1.0 55.3 20.3281 1 .3 1.0 56.3 20.4374 1 .3 1.0 57.3 21.3167 1 .3 1.0 58.3

21.3331 1 .3 1.0 59.2 21.4956 1 .3 1.0 60.2 21.5342 1 .3 1.0 61.2 21.8580 1 .3 1.0 62.1 22.0558 1 .3 1.0 63.1 22.7613 1 .3 1.0 64.1 23.0378 1 .3 1.0 65.0 23.0509 1 .3 1.0 66.0 23.5488 1 .3 1.0 67.0

23.5536 1 .3 1.0 68.0 23.5756 1 .3 1.0 68.9 23.9402 1 .3 1.0 69.9 24.0118 1 .3 1.0 70.9 24.0371 1 .3 1.0 71.8 24.2292 1 .3 1.0 72.8 24.6272 1 .3 1.0 73.8 24.7953 1 .3 1.0 74.8 25.0377 1 .3 1.0 75.7

25.6405 1 .3 1.0 76.7 26.2260 1 .3 1.0 77.7 26.3271 1 .3 1.0 78.6 26.4154 1 .3 1.0 79.6 195

26.4370 1 .3 1.0 80.6 26.6752 1 .3 1.0 81.6 26.7027 1 .3 1.0 82.5 27.0707 1 .3 1.0 83.5 27.2741 1 .3 1.0 84.5 28.7704 1 .3 1.0 85.4

29.2672 1 .3 1.0 86.4 29.3823 1 .3 1.0 87.4 29.6808 1 .3 1.0 88.3 29.8826 1 .3 1.0 89.3 29.9002 1 .3 1.0 90.3 30.1704 1 .3 1.0 91.3 30.7329 1 .3 1.0 92.2 31.1675 2 .7 1.9 94.2 33.0183 1 .3 1.0 95.1

35.3169 1 .3 1.0 96.1 37.7712 1 .3 1.0 97.1 37.9833 1 .3 1.0 98.1 39.8529 1 .3 1.0 99.0 43.0358 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

196

Statistics

3DIMP 3DMP 3DILP 3DLP N Valid 103 103 103 103 Missing 197 197 197 197 Mean 12.964364 9.617129 6.601665 6.475816 Median 11.926300 8.990100 6.508800 6.446000 Mode 2.8957a 4.5614a 2.0699a 2.8644a Std. Deviation 6.0540780 3.2226535 2.0132736 1.7930358 Variance 36.652 10.385 4.053 3.215 Range 37.9863 15.8393 11.3377 7.9632 Minimum 2.8957 4.5614 2.0699 2.8644 Maximum 40.8820 20.4007 13.4076 10.8276 Percentiles 25 9.278300 7.281800 5.122700 5.215700 50 11.926300 8.990100 6.508800 6.446000 75 15.893400 11.670500 7.880500 7.830900 a. Multiple modes exist. The smallest value is shown

Frequency Table

3DMP

Cumulative Frequency Percent Valid Percent Percent Valid 4.5614 1 .3 1.0 1.0 4.6044 1 .3 1.0 1.9 4.7593 1 .3 1.0 2.9

4.7748 1 .3 1.0 3.9 4.8528 1 .3 1.0 4.9 5.2719 1 .3 1.0 5.8

197

5.3667 1 .3 1.0 6.8 5.4307 1 .3 1.0 7.8 5.8005 1 .3 1.0 8.7 5.8520 1 .3 1.0 9.7 6.0116 1 .3 1.0 10.7 6.1013 1 .3 1.0 11.7

6.1227 1 .3 1.0 12.6 6.1456 1 .3 1.0 13.6 6.1619 1 .3 1.0 14.6 6.6674 1 .3 1.0 15.5 6.8289 1 .3 1.0 16.5 6.8504 1 .3 1.0 17.5 6.9524 1 .3 1.0 18.4 6.9995 1 .3 1.0 19.4 7.0862 1 .3 1.0 20.4

7.0909 1 .3 1.0 21.4 7.1086 1 .3 1.0 22.3 7.1297 1 .3 1.0 23.3 7.2648 1 .3 1.0 24.3 7.2818 1 .3 1.0 25.2 7.5630 1 .3 1.0 26.2 7.5720 1 .3 1.0 27.2 7.6212 1 .3 1.0 28.2 7.6618 1 .3 1.0 29.1

7.6855 1 .3 1.0 30.1 7.6973 1 .3 1.0 31.1 7.7443 1 .3 1.0 32.0 7.7589 1 .3 1.0 33.0 198

7.7919 1 .3 1.0 34.0 7.7938 1 .3 1.0 35.0 7.8071 1 .3 1.0 35.9 7.8419 1 .3 1.0 36.9 7.8542 1 .3 1.0 37.9 7.8767 1 .3 1.0 38.8

8.0852 1 .3 1.0 39.8 8.1535 1 .3 1.0 40.8 8.2241 1 .3 1.0 41.7 8.3653 1 .3 1.0 42.7 8.3734 1 .3 1.0 43.7 8.5647 1 .3 1.0 44.7 8.5994 1 .3 1.0 45.6 8.7157 1 .3 1.0 46.6 8.8246 1 .3 1.0 47.6

8.8684 1 .3 1.0 48.5 8.8793 1 .3 1.0 49.5 8.9901 1 .3 1.0 50.5 9.0445 1 .3 1.0 51.5 9.1379 1 .3 1.0 52.4 9.1691 1 .3 1.0 53.4 9.3209 1 .3 1.0 54.4 9.3982 1 .3 1.0 55.3 9.4037 1 .3 1.0 56.3

9.4395 1 .3 1.0 57.3 9.5559 1 .3 1.0 58.3 9.7164 1 .3 1.0 59.2 9.8422 1 .3 1.0 60.2 199

9.9128 1 .3 1.0 61.2 10.1389 1 .3 1.0 62.1 10.4916 1 .3 1.0 63.1 10.5684 1 .3 1.0 64.1 10.6125 1 .3 1.0 65.0 10.6940 1 .3 1.0 66.0

10.7446 1 .3 1.0 67.0 10.8531 1 .3 1.0 68.0 10.8907 1 .3 1.0 68.9 10.9949 1 .3 1.0 69.9 10.9986 1 .3 1.0 70.9 11.1344 1 .3 1.0 71.8 11.4433 1 .3 1.0 72.8 11.4949 1 .3 1.0 73.8 11.6240 1 .3 1.0 74.8

11.6705 1 .3 1.0 75.7 11.7563 1 .3 1.0 76.7 11.9182 1 .3 1.0 77.7 12.0562 1 .3 1.0 78.6 12.2013 1 .3 1.0 79.6 12.2932 1 .3 1.0 80.6 12.4358 1 .3 1.0 81.6 12.4498 1 .3 1.0 82.5 12.5714 1 .3 1.0 83.5

12.6224 1 .3 1.0 84.5 12.7154 1 .3 1.0 85.4 13.0037 1 .3 1.0 86.4 13.3607 1 .3 1.0 87.4 200

13.4628 1 .3 1.0 88.3 13.6922 1 .3 1.0 89.3 13.7601 1 .3 1.0 90.3 13.8180 1 .3 1.0 91.3 14.2260 1 .3 1.0 92.2 14.2524 1 .3 1.0 93.2

14.4782 1 .3 1.0 94.2 15.5891 1 .3 1.0 95.1 16.0596 1 .3 1.0 96.1 16.7418 1 .3 1.0 97.1 17.2837 1 .3 1.0 98.1 19.1283 1 .3 1.0 99.0 20.4007 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

3DIMP

Cumulative Frequency Percent Valid Percent Percent Valid 2.8957 1 .3 1.0 1.0 4.9412 1 .3 1.0 1.9 5.6478 1 .3 1.0 2.9 5.7220 1 .3 1.0 3.9

5.9025 1 .3 1.0 4.9 5.9098 1 .3 1.0 5.8 6.1411 1 .3 1.0 6.8 6.3072 1 .3 1.0 7.8 201

6.4637 1 .3 1.0 8.7 6.4933 1 .3 1.0 9.7 6.6744 1 .3 1.0 10.7 6.7209 1 .3 1.0 11.7 7.0569 1 .3 1.0 12.6 7.1193 1 .3 1.0 13.6

7.2969 1 .3 1.0 14.6 7.3508 1 .3 1.0 15.5 7.4614 1 .3 1.0 16.5 8.1311 1 .3 1.0 17.5 8.2109 1 .3 1.0 18.4 8.2812 1 .3 1.0 19.4 8.3882 1 .3 1.0 20.4 8.8104 1 .3 1.0 21.4 8.8598 1 .3 1.0 22.3

9.0339 1 .3 1.0 23.3 9.1654 1 .3 1.0 24.3 9.2783 1 .3 1.0 25.2 9.3579 1 .3 1.0 26.2 9.4170 1 .3 1.0 27.2 9.4430 1 .3 1.0 28.2 9.4993 1 .3 1.0 29.1 9.5015 1 .3 1.0 30.1 9.5195 1 .3 1.0 31.1

9.6526 1 .3 1.0 32.0 9.6530 1 .3 1.0 33.0 9.6810 1 .3 1.0 34.0 9.9007 1 .3 1.0 35.0 202

10.0881 1 .3 1.0 35.9 10.2545 1 .3 1.0 36.9 10.2961 1 .3 1.0 37.9 10.3749 1 .3 1.0 38.8 10.3988 1 .3 1.0 39.8 10.5623 1 .3 1.0 40.8

10.7916 1 .3 1.0 41.7 10.8102 1 .3 1.0 42.7 10.8268 1 .3 1.0 43.7 10.9015 1 .3 1.0 44.7 11.1715 1 .3 1.0 45.6 11.3605 1 .3 1.0 46.6 11.3930 1 .3 1.0 47.6 11.5082 1 .3 1.0 48.5 11.5976 1 .3 1.0 49.5

11.9263 1 .3 1.0 50.5 11.9716 1 .3 1.0 51.5 12.0801 1 .3 1.0 52.4 12.1270 1 .3 1.0 53.4 12.1579 1 .3 1.0 54.4 12.2392 1 .3 1.0 55.3 12.5969 1 .3 1.0 56.3 12.6297 1 .3 1.0 57.3 12.6639 1 .3 1.0 58.3

12.7566 1 .3 1.0 59.2 12.9002 1 .3 1.0 60.2 12.9683 1 .3 1.0 61.2 13.0383 1 .3 1.0 62.1 203

13.0422 1 .3 1.0 63.1 13.5424 1 .3 1.0 64.1 13.9803 1 .3 1.0 65.0 14.1103 1 .3 1.0 66.0 14.5475 1 .3 1.0 67.0 14.5610 1 .3 1.0 68.0

14.6155 1 .3 1.0 68.9 14.6769 1 .3 1.0 69.9 14.8406 1 .3 1.0 70.9 15.0226 1 .3 1.0 71.8 15.4435 1 .3 1.0 72.8 15.5250 1 .3 1.0 73.8 15.5377 1 .3 1.0 74.8 15.8934 1 .3 1.0 75.7 16.1110 1 .3 1.0 76.7

16.2584 1 .3 1.0 77.7 16.7390 1 .3 1.0 78.6 16.8468 1 .3 1.0 79.6 17.0486 1 .3 1.0 80.6 17.1437 1 .3 1.0 81.6 17.1939 1 .3 1.0 82.5 17.2669 1 .3 1.0 83.5 17.3537 1 .3 1.0 84.5 17.5483 1 .3 1.0 85.4

17.6208 1 .3 1.0 86.4 18.0563 1 .3 1.0 87.4 20.3369 1 .3 1.0 88.3 20.5036 1 .3 1.0 89.3 204

20.5269 1 .3 1.0 90.3 20.7282 1 .3 1.0 91.3 20.9856 1 .3 1.0 92.2 21.6821 1 .3 1.0 93.2 22.0469 1 .3 1.0 94.2 22.1634 1 .3 1.0 95.1

22.1972 1 .3 1.0 96.1 23.4168 1 .3 1.0 97.1 27.1417 1 .3 1.0 98.1 37.9107 1 .3 1.0 99.0 40.8820 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

3DLP

Cumulative Frequency Percent Valid Percent Percent Valid 2.8644 1 .3 1.0 1.0 2.9541 1 .3 1.0 1.9 2.9930 1 .3 1.0 2.9 3.1642 1 .3 1.0 3.9 3.2621 1 .3 1.0 4.9

3.5186 1 .3 1.0 5.8 3.7444 1 .3 1.0 6.8 3.7450 1 .3 1.0 7.8

205

3.8950 1 .3 1.0 8.7 4.0427 1 .3 1.0 9.7 4.3448 1 .3 1.0 10.7 4.3873 1 .3 1.0 11.7 4.4144 1 .3 1.0 12.6 4.4150 1 .3 1.0 13.6

4.5293 1 .3 1.0 14.6 4.5934 1 .3 1.0 15.5 4.6122 1 .3 1.0 16.5 4.6662 1 .3 1.0 17.5 4.8136 1 .3 1.0 18.4 4.8496 1 .3 1.0 19.4 4.9304 1 .3 1.0 20.4 5.0483 1 .3 1.0 21.4 5.0641 1 .3 1.0 22.3

5.1127 1 .3 1.0 23.3 5.1179 1 .3 1.0 24.3 5.2157 1 .3 1.0 25.2 5.2734 1 .3 1.0 26.2 5.3030 1 .3 1.0 27.2 5.3264 1 .3 1.0 28.2 5.3376 1 .3 1.0 29.1 5.3723 1 .3 1.0 30.1 5.3777 1 .3 1.0 31.1

5.4378 1 .3 1.0 32.0 5.4408 1 .3 1.0 33.0 5.4699 1 .3 1.0 34.0 5.5483 1 .3 1.0 35.0 206

5.6102 1 .3 1.0 35.9 5.6826 1 .3 1.0 36.9 5.7005 1 .3 1.0 37.9 5.7052 1 .3 1.0 38.8 5.8897 1 .3 1.0 39.8 5.9502 1 .3 1.0 40.8

6.0494 1 .3 1.0 41.7 6.0600 1 .3 1.0 42.7 6.0977 1 .3 1.0 43.7 6.1076 1 .3 1.0 44.7 6.1696 1 .3 1.0 45.6 6.3069 1 .3 1.0 46.6 6.3512 1 .3 1.0 47.6 6.4092 1 .3 1.0 48.5 6.4376 1 .3 1.0 49.5

6.4460 1 .3 1.0 50.5 6.5049 1 .3 1.0 51.5 6.5231 1 .3 1.0 52.4 6.5585 1 .3 1.0 53.4 6.5867 1 .3 1.0 54.4 6.6939 1 .3 1.0 55.3 6.7676 1 .3 1.0 56.3 6.7867 1 .3 1.0 57.3 6.8723 1 .3 1.0 58.3

6.9394 1 .3 1.0 59.2 6.9474 1 .3 1.0 60.2 7.0221 1 .3 1.0 61.2 7.1303 1 .3 1.0 62.1 207

7.1770 1 .3 1.0 63.1 7.2095 1 .3 1.0 64.1 7.2717 1 .3 1.0 65.0 7.2891 1 .3 1.0 66.0 7.2903 1 .3 1.0 67.0 7.3742 1 .3 1.0 68.0

7.4437 1 .3 1.0 68.9 7.5494 1 .3 1.0 69.9 7.5574 1 .3 1.0 70.9 7.5742 1 .3 1.0 71.8 7.7254 1 .3 1.0 72.8 7.7484 1 .3 1.0 73.8 7.8048 1 .3 1.0 74.8 7.8309 1 .3 1.0 75.7 7.8341 1 .3 1.0 76.7

7.8912 1 .3 1.0 77.7 7.9138 1 .3 1.0 78.6 7.9547 1 .3 1.0 79.6 7.9846 1 .3 1.0 80.6 8.0057 1 .3 1.0 81.6 8.0539 1 .3 1.0 82.5 8.1766 1 .3 1.0 83.5 8.2773 1 .3 1.0 84.5 8.2805 1 .3 1.0 85.4

8.3687 1 .3 1.0 86.4 8.3816 1 .3 1.0 87.4 8.4980 1 .3 1.0 88.3 8.5311 1 .3 1.0 89.3 208

8.7401 1 .3 1.0 90.3 8.8562 1 .3 1.0 91.3 8.8662 1 .3 1.0 92.2 8.9770 1 .3 1.0 93.2 9.2722 1 .3 1.0 94.2 9.8238 1 .3 1.0 95.1

9.8794 1 .3 1.0 96.1 9.9758 1 .3 1.0 97.1 10.0017 1 .3 1.0 98.1 10.2991 1 .3 1.0 99.0 10.8276 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

3DILP

Cumulative Frequency Percent Valid Percent Percent Valid 2.0699 1 .3 1.0 1.0 2.7886 1 .3 1.0 1.9 2.9334 1 .3 1.0 2.9 3.2830 1 .3 1.0 3.9 3.3613 1 .3 1.0 4.9 3.6010 1 .3 1.0 5.8

3.6208 1 .3 1.0 6.8 3.8219 1 .3 1.0 7.8 3.8749 1 .3 1.0 8.7 4.0678 1 .3 1.0 9.7 209

4.1271 1 .3 1.0 10.7 4.1408 1 .3 1.0 11.7 4.2872 1 .3 1.0 12.6 4.2993 1 .3 1.0 13.6 4.4161 1 .3 1.0 14.6 4.4507 1 .3 1.0 15.5

4.5229 1 .3 1.0 16.5 4.5299 1 .3 1.0 17.5 4.6870 1 .3 1.0 18.4 4.7923 1 .3 1.0 19.4 4.8134 1 .3 1.0 20.4 4.8286 1 .3 1.0 21.4 4.9501 1 .3 1.0 22.3 5.0165 1 .3 1.0 23.3 5.0694 1 .3 1.0 24.3

5.1227 1 .3 1.0 25.2 5.1520 1 .3 1.0 26.2 5.2635 1 .3 1.0 27.2 5.2780 1 .3 1.0 28.2 5.4316 1 .3 1.0 29.1 5.4360 1 .3 1.0 30.1 5.5160 1 .3 1.0 31.1 5.5602 1 .3 1.0 32.0 5.5639 1 .3 1.0 33.0

5.6211 1 .3 1.0 34.0 5.6768 1 .3 1.0 35.0 5.6786 1 .3 1.0 35.9 5.7120 1 .3 1.0 36.9 210

5.9584 1 .3 1.0 37.9 5.9837 1 .3 1.0 38.8 6.0312 1 .3 1.0 39.8 6.0641 1 .3 1.0 40.8 6.0949 1 .3 1.0 41.7 6.2335 1 .3 1.0 42.7

6.2689 1 .3 1.0 43.7 6.2817 1 .3 1.0 44.7 6.3020 1 .3 1.0 45.6 6.3141 1 .3 1.0 46.6 6.3193 1 .3 1.0 47.6 6.4479 1 .3 1.0 48.5 6.4757 1 .3 1.0 49.5 6.5088 1 .3 1.0 50.5 6.5405 1 .3 1.0 51.5

6.5796 1 .3 1.0 52.4 6.6199 1 .3 1.0 53.4 6.6376 1 .3 1.0 54.4 6.8346 1 .3 1.0 55.3 6.8520 1 .3 1.0 56.3 6.8591 1 .3 1.0 57.3 6.9743 1 .3 1.0 58.3 6.9763 1 .3 1.0 59.2 7.0208 1 .3 1.0 60.2

7.0903 1 .3 1.0 61.2 7.0948 1 .3 1.0 62.1 7.1995 1 .3 1.0 63.1 7.2575 1 .3 1.0 64.1 211

7.2961 1 .3 1.0 65.0 7.3203 1 .3 1.0 66.0 7.3232 1 .3 1.0 67.0 7.3321 1 .3 1.0 68.0 7.3901 1 .3 1.0 68.9 7.3909 1 .3 1.0 69.9

7.5449 1 .3 1.0 70.9 7.5682 1 .3 1.0 71.8 7.7304 1 .3 1.0 72.8 7.7388 1 .3 1.0 73.8 7.8657 1 .3 1.0 74.8 7.8805 1 .3 1.0 75.7 7.8838 1 .3 1.0 76.7 7.9315 1 .3 1.0 77.7 8.2766 1 .3 1.0 78.6

8.4957 1 .3 1.0 79.6 8.6906 1 .3 1.0 80.6 8.7508 1 .3 1.0 81.6 8.7738 1 .3 1.0 82.5 8.8464 1 .3 1.0 83.5 8.9047 1 .3 1.0 84.5 8.9695 1 .3 1.0 85.4 9.0584 1 .3 1.0 86.4 9.0666 1 .3 1.0 87.4

9.0706 1 .3 1.0 88.3 9.0969 1 .3 1.0 89.3 9.0980 1 .3 1.0 90.3 9.3223 1 .3 1.0 91.3 212

9.3740 1 .3 1.0 92.2 9.3831 1 .3 1.0 93.2 9.4124 1 .3 1.0 94.2 9.7170 1 .3 1.0 95.1 9.7670 1 .3 1.0 96.1 10.1381 1 .3 1.0 97.1

10.2168 1 .3 1.0 98.1 10.7508 1 .3 1.0 99.0 13.4076 1 .3 1.0 100.0 Total 103 34.3 100.0 Missing System 197 65.7 Total 300 100.0

213

Appendix C: Analytic Statistics – Second surgery

T-Test

Group Statistics

Second_surgery N Mean Std. Deviation Std. Error Mean age_ No 92 39.00 12.395 1.292 Yes 11 42.09 14.286 4.307

Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Sig. (2- Mean Std. Error Difference F Sig. t df tailed) Difference Difference Lower Upper age_ Equal variances .219 .641 -.769 101 .444 -3.091 4.018 -11.062 4.880 assumed Equal variances -.687 11.870 .505 -3.091 4.497 -12.901 6.719 not assumed

Crosstabs

Case Processing Summary

Cases Valid Missing Total

N Percent N Percent N Percent gender * Second_surgery 103 34.3% 197 65.7% 300 100.0% gender * Second_surgery Crosstabulation

214

Count Second_surgery No Yes Total gender Female 44 7 51 Male 48 4 52 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .982a 1 .322 Continuity Correctionb .452 1 .501 Likelihood Ratio .993 1 .319 Fisher's Exact Test .358 .251 Linear-by-Linear Association .973 1 .324 N of Valid Cases 103 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.45. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent race * Second_surgery 103 34.3% 197 65.7% 300 100.0% race * Second_surgery Crosstabulation Count Second_surgery No Yes Total race African-American 23 4 27 215

White 69 7 76 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .656a 1 .418 Continuity Correctionb .200 1 .655 Likelihood Ratio .617 1 .432 Fisher's Exact Test .473 .315 Linear-by-Linear Association .650 1 .420 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 2.88. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent diabetes_yn * 103 34.3% 197 65.7% 300 100.0% Second_surgery

diabetes_yn * Second_surgery Crosstabulation Count Second_surgery

No Yes Total diabetes_yn No 83 9 92 Yes 9 2 11 Total 92 11 103

216

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .727a 1 .394 Continuity Correctionb .113 1 .737 Likelihood Ratio .629 1 .428 Fisher's Exact Test .333 .333 Linear-by-Linear Association .720 1 .396 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 1.17. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases

Valid Missing Total N Percent N Percent N Percent hypertension_yn * 103 34.3% 197 65.7% 300 100.0% Second_surgery hypertension_yn * Second_surgery Crosstabulation Count Second_surgery No Yes Total hypertension_yn No 76 8 84

Yes 16 3 19 Total 92 11 103 Chi-Square Tests

217

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .638a 1 .425 Continuity Correctionb .150 1 .699 Likelihood Ratio .583 1 .445 Fisher's Exact Test .421 .328 Linear-by-Linear Association .632 1 .427 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 2.03. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total

N Percent N Percent N Percent Renal_disease * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Renal_disease * Second_surgery Crosstabulation Count Second_surgery No Yes Total Renal_disease No 90 10 100

Yes 2 1 3 Total 92 11 103 Chi-Square Tests

218

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square 1.662a 1 .197 Continuity Correctionb .116 1 .733 Likelihood Ratio 1.156 1 .282 Fisher's Exact Test .290 .290 Linear-by-Linear Association 1.646 1 .199 N of Valid Cases 103 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is .32. b. Computed only for a 2x2 table

Crosstabs

Case Processing Summary

Cases Valid Missing Total

N Percent N Percent N Percent cancer_yn * Second_surgery 103 34.3% 197 65.7% 300 100.0% cancer_yn * Second_surgery Crosstabulation Count Second_surgery No Yes Total cancer_yn No 87 11 98 Yes 5 0 5 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

219

Pearson Chi-Square .628a 1 .428 Continuity Correctionb .003 1 .960 Likelihood Ratio 1.159 1 .282 Fisher's Exact Test 1.000 .562 Linear-by-Linear Association .622 1 .430 N of Valid Cases 103 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is .53. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Multiple_Space_infection * 103 34.3% 197 65.7% 300 100.0% Second_surgery Multiple_Space_infection * Second_surgery Crosstabulation Count Second_surgery No Yes Total Multiple_Space_infection No 10 0 10 Yes 82 11 93 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square 1.324a 1 .250 Continuity Correctionb .375 1 .541 Likelihood Ratio 2.384 1 .123

220

Fisher's Exact Test .595 .306 Linear-by-Linear Association 1.311 1 .252 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 1.07. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Buccal_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery Buccal_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Buccal_space No 77 8 85 Yes 15 3 18 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .820a 1 .365 Continuity Correctionb .236 1 .627 Likelihood Ratio .738 1 .390 Fisher's Exact Test .402 .295 Linear-by-Linear Association .812 1 .368

221

N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 1.92. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Retromolar_trigone_region * 103 34.3% 197 65.7% 300 100.0% Second_surgery Mediastinum * Second_surgery Crosstabulation Count Second_surgery No Yes Total

Mediastinum No 85 11 96 Yes 7 0 7 Total 92 11 103

Retromolar_trigone_region * Second_surgery Crosstabulation Count Second_surgery No Yes Total

Retromolar_trigone_region No 86 8 94 Yes 6 3 9 Total 92 11 103

222

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square 5.305a 1 .021 Continuity Correctionb 3.022 1 .082 Likelihood Ratio 3.814 1 .051 Fisher's Exact Test .054 .054 Linear-by-Linear Association 5.254 1 .022 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .96. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total

N Percent N Percent N Percent Submental_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery Submental_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total

Submental_space No 55 3 58 Yes 37 8 45 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

223

Pearson Chi-Square 4.221a 1 .040 Continuity Correctionb 3.003 1 .083 Likelihood Ratio 4.258 1 .039 Fisher's Exact Test .055 .042 Linear-by-Linear Association 4.180 1 .041 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 4.81. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Sublingual_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery Sublingual_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Sublingual_space No 48 3 51 Yes 44 8 52 Total 92 11 103 Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

Pearson Chi-Square 2.437a 1 .118 Continuity Correctionb 1.543 1 .214 Likelihood Ratio 2.523 1 .112 Fisher's Exact Test .201 .106 224

Linear-by-Linear Association 2.413 1 .120 N of Valid Cases 103 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.45. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Parotid_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Parotid_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Parotid_space No 86 9 95 Yes 6 2 8 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

Pearson Chi-Square 1.865a 1 .172 Continuity Correctionb .592 1 .442 Likelihood Ratio 1.455 1 .228 Fisher's Exact Test .203 .203 225

Linear-by-Linear Association 1.847 1 .174 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .85. b. Computed only for a 2x2 table

Crosstabs

Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Masticator_space_ * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Masticator_space_ * Second_surgery Crosstabulation Count Second_surgery No Yes Total Masticator_space_ No 53 4 57 Yes 39 7 46 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square 1.794a 1 .180 Continuity Correctionb 1.038 1 .308 Likelihood Ratio 1.790 1 .181

226

Fisher's Exact Test .212 .154 Linear-by-Linear Association 1.777 1 .183 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 4.91. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Parapharyngeal_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Parapharyngeal_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Parapharyngeal_space No 63 7 70 Yes 29 4 33 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

Pearson Chi-Square .106a 1 .745 Continuity Correctionb .000 1 1.000 Likelihood Ratio .104 1 .747 Fisher's Exact Test .742 .493 227

Linear-by-Linear Association .105 1 .746 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.52. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Carotid_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Carotid_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Carotid_space No 84 10 94 Yes 8 1 9 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .002a 1 .965

228

Continuity Correctionb .000 1 1.000 Likelihood Ratio .002 1 .965 Fisher's Exact Test 1.000 .654 Linear-by-Linear Association .002 1 .965 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .96. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Retropharyngeal_space * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Retropharyngeal_space * Second_surgery Crosstabulation Count Second_surgery No Yes Total Retropharyngeal_space No 86 11 97 Yes 6 0 6 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .762a 1 .383 229

Continuity Correctionb .037 1 .848 Likelihood Ratio 1.399 1 .237 Fisher's Exact Test 1.000 .499 Linear-by-Linear Association .754 1 .385 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .64. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Danger_space_ * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Danger_space_ * Second_surgery Crosstabulation Count Second_surgery No Yes Total Danger_space_ No 91 11 102 Yes 1 0 1 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .121a 1 .728 Continuity Correctionb .000 1 1.000

230

Likelihood Ratio .227 1 .634 Fisher's Exact Test 1.000 .893 Linear-by-Linear Association .120 1 .730 N of Valid Cases 103 a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is .11. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Mediastinum * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Mediastinum * Second_surgery Crosstabulation Count Second_surgery No Yes Total Mediastinum No 85 11 96 Yes 7 0 7 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

231

Pearson Chi-Square .898a 1 .343 Continuity Correctionb .098 1 .754 Likelihood Ratio 1.641 1 .200 Fisher's Exact Test 1.000 .442 Linear-by-Linear Association .889 1 .346 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .75. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Air_way_obstruction * 103 34.3% 197 65.7% 300 100.0% Second_surgery

Air_way_obstruction * Second_surgery Crosstabulation Count Second_surgery

No Yes Total Air_way_obstruction No 75 8 83 Yes 17 3 20 Total 92 11 103 232

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .486a 1 .486 Continuity Correctionb .086 1 .769 Likelihood Ratio .450 1 .502 Fisher's Exact Test .443 .362 Linear-by-Linear Association .481 1 .488 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 2.14. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Abscess * Second_surgery 103 34.3% 197 65.7% 300 100.0%

Abscess * Second_surgery Crosstabulation Count Second_surgery

No Yes Total Abscess No 38 4 42 Yes 54 7 61 Total 92 11 103 233

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .099a 1 .753 Continuity Correctionb .000 1 1.000 Likelihood Ratio .100 1 .751 Fisher's Exact Test 1.000 .510 Linear-by-Linear Association .098 1 .754 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 4.49. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent Cellulitis * Second_surgery 103 34.3% 197 65.7% 300 100.0%

Cellulitis * Second_surgery Crosstabulation Count Second_surgery

No Yes Total Cellulitis No 54 7 61

Yes 38 4 42

234

Total 92 11 103

Chi-Square Tests

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent enlargement_submandibular 103 34.3% 197 65.7% 300 100.0% * Second_surgery

enlargement_submandibular * Second_surgery Crosstabulation Count Second_surgery No Yes Total enlargement_submandibular No 68 6 74 235

Yes 24 5 29 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided)

Pearson Chi-Square 1.822a 1 .177 Continuity Correctionb .990 1 .320 Likelihood Ratio 1.682 1 .195 Fisher's Exact Test .285 .159 Linear-by-Linear Association 1.804 1 .179 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.10. b. Computed only for a 2x2 table

Crosstabs

Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent enlargement_parotid * 103 34.3% 197 65.7% 300 100.0% Second_surgery

236

enlargement_parotid * Second_surgery Crosstabulation Count Second_surgery No Yes Total enlargement_parotid No 87 10 97 Yes 5 1 6 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .239a 1 .625 Continuity Correctionb .000 1 1.000 Likelihood Ratio .210 1 .646 Fisher's Exact Test .501 .501 Linear-by-Linear .237 1 .626 Association N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .64. b. Computed only for a 2x2 table

Crosstabs Case Processing Summary

Cases Valid Missing Total N Percent N Percent N Percent

Trismus * Second_surgery 103 34.3% 197 65.7% 300 100.0% Trismus * Second_surgery Crosstabulation Count

237

Second_surgery No Yes Total Trismus No 85 11 96 Yes 7 0 7 Total 92 11 103

Chi-Square Tests

Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) Pearson Chi-Square .898a 1 .343 Continuity Correctionb .098 1 .754 Likelihood Ratio 1.641 1 .200 Fisher's Exact Test 1.000 .442 Linear-by-Linear Association .889 1 .346 N of Valid Cases 103 a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is .75. b. Computed only for a 2x2 table

T-Test Group Statistics

Second_surgery N Mean Std. Deviation Std. Error Mean IM3D% No 136.9480182298 52.66414643078 5.490616817415 92 3933% 8726% 948% Yes 158.5137280990 76.46267407798 23.05436367126 11 3580% 5340% 4674% IMP3D% No 137.5217727713 61.32657358511 6.393737278613 92 1622% 0850% 734%

238

Yes 166.1388554544 85.93809182466 25.91130961650 11 2184% 8100% 2994% ILP3D% No 104.0902548492 22.31236601934 2.326224963375 92 5334% 4815% 335% Yes 97.73347464539 23.58142164277 7.110066146568 11 403% 4790% 202% ROI_volume No 68 10.881187 10.2718755 1.2456479 Yes 11 8.947882 7.0751565 2.1332399

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper IM3D% Equal - - 17.6989 13.5441 variances - 21.5657 56.6755 2.465 .120 101 .226 1388670 743264 assumed 1.218 0986919 9406488 2442% 89042% 6468% 1980% Equal - - 23.6991 30.5032 variances not 11.16 21.5657 73.6346 -.910 .382 6786139 411598 assumed 3 0986919 6089825 5880% 64288% 6468% 7220% IMP3D Equal - - 20.4770 12.0037 % variances - 28.6170 69.2379 1.930 .168 101 .165 0636132 896835 assumed 1.398 8268310 5504977 5240% 65593% 5620% 6840% 239

Equal - - 26.6884 29.9648 variances not - 11.25 28.6170 87.1990 .306 9644379 789956 assumed 1.072 0 8268310 4436190 0450% 94306% 5620% 5550% ILP3D Equal - 6.35678 7.15936 20.5590 % variances 7.84548 .000 .986 .888 101 .377 0203859 9016225 424829 assumed 2075224 316% 975% 43475% 842% Equal - 6.35678 7.48093 22.6209 variances not 12.24 9.90739 .850 .412 0203859 3310009 549489 assumed 0 4541269 316% 238% 88160% 529% ROI_vo Equal - 1.93330 3.22226 8.34964 lume variances .697 .406 .600 77 .550 4.48303 49 20 83 assumed 84 Equal - 17.67 1.93330 2.47029 7.13004 variances not .783 .444 3.26343 5 49 38 40 assumed 41

Regression

Descriptive Statistics

Mean Std. Deviation N Second_surgery .14 .348 79 IM3D% 145.7059775013 57.50197409269 79 6528% 8006% IMP3D% 146.6821883979 68.19503604127 79 4918% 3470% 240

ILP3D% 102.6069957010 20.30669929804 79 3040% 3107% ROI_volume 10.611992 9.8743604 79

Correlations

Second_surg ery IM3D% IMP3D% ILP3D% ROI_volume Pearson Second_surgery 1.000 .090 .115 -.097 -.068 Correlation IM3D% .090 1.000 .396 .093 .308

IMP3D% .115 .396 1.000 .415 -.018

ILP3D% -.097 .093 .415 1.000 -.087

ROI_volume -.068 .308 -.018 -.087 1.000

Sig. (1-tailed) Second_surgery . .215 .155 .197 .275

IM3D% .215 . .000 .209 .003

IMP3D% .155 .000 . .000 .438

ILP3D% .197 .209 .000 . .223

ROI_volume .275 .003 .438 .223 .

N Second_surgery

79 79 79 79 79

241

IM3D% 79 79 79 79 79

IMP3D% 79 79 79 79 79

ILP3D% 79 79 79 79 79

ROI_volume 79 79 79 79 79

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 ROI_volume, IMP3D%, . Enter ILP3D%, IM3D%b a. Dependent Variable: Second_surgery b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .223a .050 -.002 .349 a. Predictors: (Constant), ROI_volume, IMP3D%, ILP3D%, IM3D%

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression .469 4 .117 .965 .432b 242

Residual 8.999 74 .122 Total 9.468 78 a. Dependent Variable: Second_surgery b. Predictors: (Constant), ROI_volume, IMP3D%, ILP3D%, IM3D%

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .307 .224 1.373 .174 IM3D% .000 .001 .076 .581 .563 IMP3D% .001 .001 .158 1.153 .252 ILP3D% -.003 .002 -.179 -1.427 .158 ROI_volume -.004 .004 -.104 -.864 .390

a. Dependent Variable: Second_surgery T-Test

Group Statistics

Second_surgery N Mean Std. Deviation Std. Error Mean IM3D% No 136.948018229 52.6641464307 5.49061681741 92 83933% 88726% 5948% Yes 158.513728099 76.4626740779 23.0543636712 11 03580% 85340% 64674%

Independent Samples Test

243

Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Std. Interval of the Mean Error Difference Sig. (2- Differenc Differenc F Sig. t df tailed) e e Lower Upper IM3 Equal variances - - 17.69891 13.54417 D% assumed - 21.56570 56.67559 2.465 .120 101 .226 3886702 4326489 1.218 9869196 4064881 442% 042% 468% 980% Equal variances - - 23.69916 30.50324 not assumed 11.16 21.56570 73.63466 -.910 .382 7861395 1159864 3 9869196 0898257 880% 288% 468% 220% T-Test Group Statistics

Second_surgery N Mean Std. Deviation Std. Error Mean IMP3D% No 137.5217727713 61.32657358511 6.393737278613 92 1622% 0850% 734% Yes 166.1388554544 85.93809182466 25.91130961650 11 2184% 8100% 2994%

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

244

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper IMP3D% Equal variances - - 20.4770 12.0037 assumed - 28.6170 69.2379 1.930 .168 101 .165 0636132 8968356 1.398 8268310 5504977 5240% 5593% 5620% 6840%

Equal variances - - 26.6884 29.9648 not assumed - 11.25 28.6170 87.1990 .306 9644379 7899569 1.072 0 8268310 4436190 0450% 4306% 5620% 5550%

Group Statistics

Std. Error Second_surgery N Mean Std. Deviation Mean ILP3D% No 104.090254849 22.3123660193 2.32622496337 92 25334% 44815% 5335% Yes 97.7334746453 23.5814216427 7.11006614656 11 9403% 74790% 8202% Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper

245

ILP3D% Equal - 6.35678 7.15936 20.5590 variances 7.84548 .000 .986 .888 101 .377 0203859 9016225 4248294 assumed 2075224 316% 975% 3475% 842% Equal - 6.35678 7.48093 22.6209 variances not 12.24 9.90739 .850 .412 0203859 3310009 5494898 assumed 0 4541269 316% 238% 8160% 529% T-Test Group Statistics

Second_surgery N Mean Std. Deviation Std. Error Mean ROI_volume No 68 10.881187 10.2718755 1.2456479 Yes 11 8.947882 7.0751565 2.1332399 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper ROI_vol Equal variances ume assumed - 1.93330 3.22226 8.34964 .697 .406 .600 77 .550 4.48303 49 20 83 84

Equal variances - 17.67 1.93330 2.47029 7.13004 not assumed .783 .444 3.26343 5 49 38 40 41

246

T-Test Group Statistics

Std. Error Second_surgery N Mean Std. Deviation Mean Fluid_density No .01524226449 68 .11776031607 .125690932938 1

Yes .04941474971 11 .15820255609 .163890183896 0 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper

Fluid_d Equal - - .042664 .044513 ensity variances .765 .384 -.948 77 .346 .040442 .125397 429429 492611 assumed 240017 972645 Equal - - 11.9 .051712 .072252 variances not -.782 .449 .040442 .153137 77 127356 555251 assumed 240017 035286

247

Appendix D: Analytic statistics- LOS Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method

1 age_b . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .213a .045 .036 4.8600 a. Predictors: (Constant), age_

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression 113.058 1 113.058 4.787 .031b Residual 2385.580 101 23.620 Total 2498.639 102 a. Dependent Variable: los_days b. Predictors: (Constant), age_

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 2.312 1.580 1.463 .147

248

age_ .084 .038 .213 2.188 .031 a. Dependent Variable: los_days

T-Test Group Statistics

Std. Error gender N Mean Std. Deviation Mean los_days Female 51 5.765 6.2670 .8776 Male 52 5.449 3.2306 .4480 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper los_d Equal ays variances 1.318 .254 .323 101 .748 .3161 .9797 -1.6274 2.2596 assumed Equal 74.49 variances not .321 .749 .3161 .9853 -1.6469 2.2791 7 assumed

T-Test

Group Statistics

Std. Error race N Mean Std. Deviation Mean

249

los_days African-American 27 5.227 2.8869 .5556

White 76 5.741 5.5096 .6320

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .647 .423 -.462 101 .645 -.5139 1.1132 -2.7222 1.6943 assumed Equal 86.5 variances not -.611 .543 -.5139 .8415 -2.1866 1.1587 72 assumed

T-Test Group Statistics

diabetes_yn N Mean Std. Deviation Std. Error Mean los_days No 92 5.387 5.0115 .5225 Yes 11 7.440 4.1438 1.2494

Independent Samples Test

250

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances .845 .360 1.30 101 .195 -2.0534 1.5736 -5.1749 1.0682 assumed 5 Equal - 13.7 variances not 1.51 .152 -2.0534 1.3542 -4.9627 .8560 57 assumed 6

T-Test Group Statistics

hypertension_yn N Mean Std. Deviation Std. Error Mean los_days No 84 5.045 2.7627 .3014 Yes 19 8.086 9.7805 2.2438

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper

251

los_d Equal - ays variances 16.952 .000 2.47 101 .015 -3.0412 1.2268 -5.4748 -.6076 assumed 9 Equal - 18.6 variances not 1.34 .195 -3.0412 2.2640 -7.7857 1.7032 54 assumed 3

T-Test Group Statistics

Renal_disease N Mean Std. Deviation Std. Error Mean los_days No 100 5.506 4.9357 .4936 Yes 3 8.939 5.1267 2.9599

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances .299 .586 1.18 101 .238 -3.4330 2.8943 -9.1746 2.3085 assumed 6

252

Equal - 2.11 - variances not 1.14 .366 -3.4330 3.0008 8.8400 3 15.7061 assumed 4

T-Test Group Statistics

cancer_yn N Mean Std. Deviation Std. Error Mean los_days No 98 5.281 3.0482 .3079 Yes 5 11.974 18.6026 8.3194

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - - ays variances 64.694 .000 3.06 101 .003 -6.6935 2.1810 -2.3671 11.0200 assumed 9 Equal 4.01 - variances not -.804 .466 -6.6935 8.3251 16.3956 1 29.7827 assumed

T-Test Group Statistics

Multiple_Space_infection N Mean Std. Deviation Std. Error Mean los_days No 10 3.811 .8925 .2822 253

Yes 93 5.799 5.1666 .5357

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances 2.683 .105 1.21 101 .229 -1.9879 1.6434 -5.2480 1.2721 assumed 0 Equal - 84.0 variances not 3.28 .001 -1.9879 .6055 -3.1921 -.7838 13 assumed 3

T-Test Group Statistics

Buccal_space N Mean Std. Deviation Std. Error Mean los_days No 85 5.597 5.2503 .5695 Yes 18 5.647 3.2821 .7736

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

254

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .006 .938 -.038 101 .970 -.0493 1.2905 -2.6093 2.5107 assumed Equal 38.1 variances not -.051 .959 -.0493 .9606 -1.9937 1.8951 49 assumed

T-Test Group Statistics

Retromolar_trigone_region N Mean Std. Deviation Std. Error Mean los_days No 94 5.428 5.0459 .5204

Yes 9 7.464 3.4763 1.1588

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances .036 .849 1.18 101 .240 -2.0357 1.7236 -5.4549 1.3835 assumed 1

255

Equal - 11.5 variances not 1.60 .136 -2.0357 1.2703 -4.8164 .7450 13 assumed 3

T-Test Group Statistics

Submental_space N Mean Std. Deviation Std. Error Mean los_days No 58 4.517 2.4232 .3182 Yes 45 7.009 6.7531 1.0067

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances 9.213 .003 101 .011 -2.4918 .9565 -4.3892 -.5945 2.605 assumed Equal - 52.82 variances not .022 -2.4918 1.0558 -4.6096 -.3740 2.360 4 assumed

256

T-Test Group Statistics

Sublingual_space N Mean Std. Deviation Std. Error Mean los_days No 51 5.507 6.2314 .8726 Yes 52 5.703 3.3021 .4579

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .208 .650 -.200 101 .842 -.1963 .9800 -2.1404 1.7478 assumed Equal 75.7 variances not -.199 .843 -.1963 .9854 -2.1591 1.7664 03 assumed

T-Test Group Statistics

Std. Error Parotid_space N Mean Std. Deviation Mean los_days No 95 5.476 5.0587 .5190 Yes 8 7.144 3.2208 1.1387 257

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .000 .997 -.914 101 .363 -1.6673 1.8235 -5.2847 1.9501 assumed Equal - 10.1 variances not 1.33 .212 -1.6673 1.2514 -4.4491 1.1144 78 assumed 2

T-Test Group Statistics

Masticator_space_ N Mean Std. Deviation Std. Error Mean los_days No 57 5.680 5.9453 .7875 Yes 46 5.514 3.3946 .5005

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

258

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper los_d Equal ays variances .074 .786 .168 101 .867 .1657 .9857 -1.7896 2.1210 assumed Equal 91.75 variances not .178 .859 .1657 .9331 -1.6876 2.0189 0 assumed

T-Test Group Statistics

Parapharyngeal_space N Mean Std. Deviation Std. Error Mean los_days No 70 5.534 5.6716 .6779 Yes 33 5.759 2.9472 .5130

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .505 .479 -.215 101 .830 -.2257 1.0500 -2.3086 1.8573 assumed

259

Equal 99.96 variances not -.265 .791 -.2257 .8501 -1.9123 1.4610 4 assumed

T-Test

Group Statistics

Std. Error Carotid_space N Mean Std. Deviation Mean los_days No 94 5.356 4.8715 .5025 Yes 9 8.218 5.2948 1.7649 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances 2.775 .099 1.67 101 .098 -2.8622 1.7120 -6.2583 .5339 assumed 2

Equal - 9.34 variances not 1.56 .152 -2.8622 1.8351 -6.9902 1.2658 4 assumed 0

T-Test 260

Group Statistics

Retropharyngeal_space N Mean Std. Deviation Std. Error Mean los_days No 97 5.465 4.9363 .5012 Yes 6 7.880 5.0283 2.0528

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances .648 .423 1.16 101 .248 -2.4149 2.0786 -6.5383 1.7084 assumed 2

Equal - 5.61 variances not 1.14 .300 -2.4149 2.1131 -7.6732 2.8434 3 assumed 3

T-Test Group Statistics

Danger_space_ N Mean Std. Deviation Std. Error Mean los_days No 102 5.545 4.9355 .4887 Yes 1 11.773 . .

Independent Samples Test 261

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances . . 1.25 101 .212 -6.2275 4.9596 -16.0660 3.6110 assumed 6 Equal variances not . . . -6.2275 . . . assumed

T-Test Group Statistics

Mediastinum N Mean Std. Deviation Std. Error Mean los_days No 96 5.425 4.9121 .5013 Yes 7 8.090 5.1655 1.9524

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper

262

los_d Equal - ays variances 1.374 .244 1.38 101 .170 -2.6649 1.9291 -6.4917 1.1620 assumed 1 Equal - 6.81 variances not 1.32 .229 -2.6649 2.0157 -7.4575 2.1278 5 assumed 2

T-Test

Group Statistics

Std. Error Abscess N Mean Std. Deviation Mean los_days No 42 5.269 6.5578 1.0119

Yes 61 5.838 3.4818 .4458

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper los_d Equal ays variances .065 .800 -.572 101 .569 -.5692 .9957 -2.5444 1.4059 assumed Equal 56.99 variances not -.515 .609 -.5692 1.1057 -2.7835 1.6450 3 assumed 263

T-Test Group Statistics

Std. Error Cellulitis N Mean Std. Deviation Mean los_days No 61 5.838 3.4818 .4458 Yes 42 5.269 6.5578 1.0119 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .065 .800 .572 101 .569 .5692 .9957 -1.4059 2.5444 assumed Equal 56.9 variances not .515 .609 .5692 1.1057 -1.6450 2.7835 93 assumed

T-Test Group Statistics

enlargement_submandibul ar N Mean Std. Deviation Std. Error Mean los_days No 74 5.698 5.5594 .6463 Yes 29 5.371 2.9291 .5439 Independent Samples Test 264

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper los_d Equal variances .718 .399 .300 101 .764 .3273 1.0892 -1.8334 2.4879 ays assumed Equal variances 92.30 .387 .699 .3273 .8447 -1.3503 2.0048 not assumed 2

T-Test Group Statistics

Std. Error enlargement_parotid N Mean Std. Deviation Mean los_days No 97 5.094 2.8968 .2941 Yes 6 13.884 15.8525 6.4718 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper

265

los_d Equal - - ays variances 40.127 .000 4.62 101 .000 -8.7898 1.9009 -5.0190 12.5605 assumed 4

Equal - 5.02 - variances not 1.35 .233 -8.7898 6.4784 7.8430 1 25.4225 assumed 7

T-Test Group Statistics

Std. Error Trismus N Mean Std. Deviation Mean los_days No 96 5.430 4.9186 .5020 Yes 7 8.011 5.1134 1.9327 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances - assumed .996 .321 1.33 101 .184 -2.5801 1.9303 -6.4093 1.2490 7

Equal - 6.83 variances not 1.29 .238 -2.5801 1.9968 -7.3251 2.1648 5 assumed 2

266

T-Test Group Statistics

Std. Error Second_surgery N Mean Std. Deviation Mean los_days No 92 5.335 5.0341 .5248 Yes 11 7.873 3.5949 1.0839 Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differenc Differenc Difference F Sig. t df tailed) e e Lower Upper los_d Equal - ays variances .087 .769 101 .108 -2.5383 1.5666 -5.6459 .5694 1.620 assumed Equal - 15.14 variances not .052 -2.5383 1.2043 -5.1030 .0264 2.108 8 assumed

267

T-Test Group Statistics

Bacterial Gas N Mean Std. Deviation Std. Error Mean los_days NO 69 5.458 5.5791 .6716 Yes 34 5.907 3.3821 .5800

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal ays variances .111 .740 -.431 101 .667 -.4489 1.0412 -2.5144 1.6166 assumed Equal 96.56 variances not -.506 .614 -.4489 .8874 -2.2103 1.3125 9 assumed

T-Test

Group Statistics

Air_way_obstruction N Mean Std. Deviation Std. Error Mean los_days No 83 4.696 2.6033 .2858 Yes 20 9.382 9.1448 2.0448

268

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

Std. 95% Confidence Mean Error Interval of the Sig. (2- Differen Differen Difference F Sig. t df tailed) ce ce Lower Upper los_d Equal - ays variances 14.414 .000 4.08 101 .000 -4.6865 1.1478 -6.9635 -2.4095 assumed 3 Equal - 19.7 variances not 2.27 .035 -4.6865 2.0647 -8.9970 -.3761 48 assumed 0

Regression Descriptive Statistics

Mean Std. Deviation N los_days 5.367 3.0500 79 IM3D% 145.7059775013 57.50197409269 79 6528% 8006% IMP3D% 146.6821883979 68.19503604127 79 4918% 3470% ILP3D% 102.6069957010 20.30669929804 79 3040% 3107% ROI_volume 10.611992 9.8743604 79

269

Correlations

los_days IM3D% IMP3D% ILP3D% ROI_volume Pearson Correlation los_days 1.000 -.034 .130 -.243 .128 IM3D% -.034 1.000 .396 .093 .308 IMP3D% .130 .396 1.000 .415 -.018

ILP3D% -.243 .093 .415 1.000 -.087 ROI_volume .128 .308 -.018 -.087 1.000 Sig. (1-tailed) los_days . .382 .126 .015 .131 IM3D% .382 . .000 .209 .003 IMP3D% .126 .000 . .000 .438 ILP3D% .015 .209 .000 . .223 ROI_volume .131 .003 .438 .223 . N los_days 79 79 79 79 79

IM3D% 79 79 79 79 79 IMP3D% 79 79 79 79 79 ILP3D% 79 79 79 79 79 ROI_volume 79 79 79 79 79

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 ROI_volume, IMP3D%, . Enter ILP3D%, IM3D%b a. Dependent Variable: los_days 270

b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate

1 .402a .162 .117 2.8667 a. Predictors: (Constant), ROI_volume, IMP3D%, ILP3D%, IM3D%

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression 117.483 4 29.371 3.574 .010b Residual 608.123 74 8.218 Total 725.606 78 a. Dependent Variable: los_days b. Predictors: (Constant), ROI_volume, IMP3D%, ILP3D%, IM3D%

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 9.526 1.841 5.174 .000

IM3D% -.010 .007 -.193 -1.565 .122 IMP3D% .016 .006 .360 2.803 .006 ILP3D% -.054 .018 -.360 -3.064 .003 ROI_volume .050 .035 .162 1.430 .157

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a. Dependent Variable: los_days

Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 IM3D%b . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .058a .003 -.006 4.9654 a. Predictors: (Constant), IM3D%

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression 8.420 1 8.420 .342 .560b Residual 2490.218 101 24.656 Total 2498.639 102 a. Dependent Variable: los_days b. Predictors: (Constant), IM3D%

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 272

1 (Constant) 6.325 1.325 4.775 .000 IM3D% -.005 .009 -.058 -.584 .560 a. Dependent Variable: los_days

Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 IMP3D%b . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .042a .002 -.008 4.9693 a. Predictors: (Constant), IMP3D%

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 4.506 1 4.506 .182 .670b Residual 2494.133 101 24.694 Total 2498.639 102

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a. Dependent Variable: los_days b. Predictors: (Constant), IMP3D%

Coefficientsa

Standardized Unstandardized Coefficients Coefficients

Model B Std. Error Beta t Sig. 1 (Constant) 5.148 1.179 4.366 .000 IMP3D% .003 .008 .042 .427 .670 a. Dependent Variable: los_days

Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 ILP3D%b . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .179a .032 .022 4.8935 a. Predictors: (Constant), ILP3D%

ANOVAa

Model Sum of Squares df Mean Square F Sig.

274

1 Regression 80.028 1 80.028 3.342 .070b Residual 2418.611 101 23.947 Total 2498.639 102 a. Dependent Variable: los_days b. Predictors: (Constant), ILP3D%

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 9.692 2.287 4.239 .000 ILP3D% -.040 .022 -.179 -1.828 .070 a. Dependent Variable: los_days

Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 ROI_volumeb . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .128a .016 .004 3.0446 a. Predictors: (Constant), ROI_volume 275

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression 11.851 1 11.851 1.278 .262b Residual 713.755 77 9.270 Total 725.606 78 a. Dependent Variable: los_days b. Predictors: (Constant), ROI_volume

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 4.948 .505 9.806 .000 ROI_volume .039 .035 .128 1.131 .262 a. Dependent Variable: los_days

Regression

Variables Entered/Removeda

Variables Variables Model Entered Removed Method 1 Fluid_densityb . Enter a. Dependent Variable: los_days b. All requested variables entered.

Model Summary 276

Adjusted R Std. Error of the Model R R Square Square Estimate 1 .092a .008 -.004 3.0568 a. Predictors: (Constant), Fluid_density

ANOVAa

Model Sum of Squares df Mean Square F Sig. 1 Regression 6.122 1 6.122 .655 .421b Residual 719.485 77 9.344 Total 725.606 78 a. Dependent Variable: los_days b. Predictors: (Constant), Fluid_density

Coefficientsa

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 5.630 .474 11.890 .000 Fluid_density -2.135 2.638 -.092 -.809 .421 a. Dependent Variable: los_days

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Appendix E: LOS Plots

Explore cancer_yn

Extreme Valuesa

cancer_yn Case Number Pt_ID Value los_days No Highest 1 6 9 16.0 2 63 112 14.7 3 30 51 14.2 4 28 49 12.0 5 100 180 12.0 Lowest 1 62 110 1.3 2 3 5 1.8

3 94 168 2.0 4 95 170 2.1 5 70 123 2.2 Yes Highest 1 60 103 45.2 2 64 114 5.0 Lowest 1 31 52 2.1 2 39 66 3.8 a. The requested number of extreme values exceeds the number of data points. A smaller number of extremes is displayed.

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Boxplots

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Appendix F: Spatial Relationship

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