Dentistry-based approaches to sleep-disordered breathing, algorithms, and multidisciplinary perspectives.

Sleep disorders affect more than 20 percent of the U.S. population, but less than 7 percent have been medically diagnosed. Dentists are ideally positioned to identify many patients who fall under the grouping of sleep-disordered breathing. This paper presents perspectives on sleep-related issues from various medical specialties with a goal to broaden the dentist's appreciation of this topic and open avenues of communication. Algorithms are proposed to guide dentists following positive screenings for sleep-disordered breathing.

Education in sleep disorders in US dental schools DDS programs.

INTRODUCTION: Medical school surveys of pre-doctoral curriculum hours in the somnology, the study of sleep, and its application in sleep medicine/sleep disorders (SM) show slow progress. Limited information is available regarding dentist training. This study assessed current pre-doctoral dental education in the field of somnology with the hypothesis that increased curriculum hours are being devoted to SM but that competencies are still lacking. MATERIALS AND METHODS: The 58 US dental schools were surveyed for curriculum offered in SM in the 2008/2009 academic year using an eight-topic, 52-item questionnaire mailed to the deans. Two new dental schools with interim accreditation had not graduated a class and were not included. Responses were received from 49 of 56 (87.5%) of the remaining schools. RESULTS AND CONCLUSIONS: Results showed 75.5% of responding US dental schools reported some teaching time in SM in their pre-doctoral dental program with curriculum hours ranging from 0 to 15 h: 12 schools spent 0 h (24.5%), 26 schools 1-3 h, 5 schools 4-6 h, 3 schools 7-10 h, and 3 schools >10 h. The average number of educational hours was 3.92 h for the schools with curriculum time in SM, (2.96 across all 49 responding schools). The most frequently covered topics included sleep-related breathing disorders (32 schools) and sleep bruxism (31 schools). Although 3.92 h is an improvement from the mean 2.5 h last reported, the absolute number of curriculum hours given the epidemic scope of sleep problems still appears insufficient in most schools to achieve any competency in screening for SRBD, or sufficient foundation for future involvement in treatment.

Improved interaction models of temporomandibular joint anatomic relationships in asymptomatic subjects and patients with disc displacement with or without reduction.

AIMS: To consider temporomandibular joint (TMJ) anatomic interactions in order to refine hard tissue models differentiating (1) joints diagnosed with disc displacement with reduction (DDwR) or without reduction (DDw/oR) from asymptomatic joints (Normals), and (2) DDwR joints from DDw/oR joints. METHODS: TMJ tomograms of 84 women with unilateral DDwR and 78 with unilateral DDw/oR were compared against each other and against those of 42 female Normal joints through the use of 14 linear and angular measurements, 8 ratios, and 34 interactions. A classification tree model for each comparison was tested for fit with sensitivity, specificity, accuracy, and log likelihood and compared to logistic regression models. RESULTS: In the classification tree model comparison, the DDwR model versus the Normal model realized 35.9% log likelihood (88.0% sensitivity, 66.7% specificity); the DDw/oR model versus the Normal model realized 38.8% log likelihood (69.6% sensitivity, 85.7% specificity). The DDwR model versus the DDw/oR model realized 33.3% log likelihood (76.0% sensitivity, 73.1% specificity). In the logistic regression model comparison, the DDwR model versus the Normal model realized 40.8% log likelihood (82.1% sensitivity, 78.6% specificity) and the DDw/oR model versus the Normal model realized 61.1% log likelihood (85.9% sensitivity, 90.5% specificity). The DDwR model versus the DDw/oR model realized 21.5% log likelihood (60.3% sensitivity, 79.8% specificity). The addition of interactions to the logistic regression models improved the previously published log likelihood from 99% to 149%. CONCLUSION: The interactions improved logistic regression models and the data suggest that anatomic characteristics influence joint functional status. Because the models incorporated nearly all considered anatomic measurements, no anatomic factor is redundant in the closed TMJ biological system.

Multifactorial comparison of disk displacement with and without reduction to normals according to temporomandibular joint hard tissue anatomic relationships.

STATEMENT OF PROBLEM: There is disagreement about the predictive value of temporomandibular joint tomographic anatomy in the diagnosis of internal derangements. PURPOSE: This study aimed to identify multifactorial temporomandibular hard tissue relationships that differentiate disk displacement with reduction and disk displacement without reduction from normals. MATERIAL AND METHODS: Temporomandibular joint tomograms from females diagnosed with unilateral disk displacement with (n=84) or without (n=78) reduction were compared to 42 asymptomatic normal joints with the use of 14 linear and angular measurements and 8 ratios. A validated classification tree model was tested for accuracy with sensitivity, specificity, goodness of fit, and the amount of log likelihood accounted for. The tree model was compared with a multiple logistic regression model and univariate testing. RESULTS: The disk displacement with reduction tree model consisted of 3 disease and 2 normal pathways with interactions between fossa width to depth ratio, condyle position, and linear posterior joint space. This class was characterized by either a much wider- and shallower-than-average fossa shape and/or by a moderately posterior condyle position when the fossa shape was average to deeper and/or narrower. The logistic regression and univariate models also suggested wider and/or shallower fossae, as well as longer eminence length. The disk displacement without reduction tree model consisted of 2 disease pathways and 1 normal pathway. Interactions characterized this class by either a posterior to very posterior condyle position or by a much deeper than average fossa depth when the condyle position was concentric to anterior. The logistic regression model emphasized greater fossa depth and width versus normals. The tree models conservatively predicted the disease classes: Rescaled Cox and Snell R(2) 37.0%, sensitivity 70.2%, and specificity 90.5% for disk displacement with reduction; R(2) 28.8%, sensitivity 66.7%, and specificity 85.7% for disk displacement without reduction. CONCLUSION: Within the limitations of this study, hard tissue relationships revealed by central tomogram sections were able to model notable differences between disk displacement with reduction and disk displacement without reduction versus asymptomatic normals when temporomandibular joints were examined as a multifactorial system typified by interactions of fossa width to depth proportions and condyle position. While substantial, the hard tissue predicted only part of the biology. The model could be broadened by additional factors and interactions.

Multifactorial modeling of temporomandibular anatomic and orthopedic relationships in normal versus undifferentiated disk displacement joints.

STATEMENT OF PROBLEM: There is persistent dispute about the diagnostic value of hard tissue anatomic relationships in predicting temporomandibular joint disorders and normals. PURPOSE: The goal of this study was identification of multifactorial temporomandibular hard tissue relationships that differentiate asymptomatic normal joints. MATERIAL AND METHODS: Central section lateral tomograms of 162 female temporomandibular joints with pooled diagnoses of unilateral disk displacement with and without reduction were compared to 42 female asymptomatic normal joints using 14 linear and angular measurements and 8 ratios. A validated classification tree model was tested for accuracy with sensitivity, specificity, goodness of fit, and the amount of the log likelihood accounted for. The tree model was compared with a multiple logistic regression model and univariate testing. RESULTS: The classification tree model consisted of 3 asymptomatic and 4 disk displacement terminal nodes consisting of interactions of condyle position with measures of fossa size and shape, of which mainly average non-extreme measurements and more frequent concentric ranges typified the asymptomatic joints. The logistic regression and univariate models also incorporated condyle position and size, but the logistic regression accounted for less of the log likelihood than the tree (23.3% vs. 32.6% Rescaled Cox and Snell R(2)). The tree and the logistic regression models were moderately good predictors for distinguishing normals from disk displacement joints (sensitivity 67.9% and 72.2%, specificity 85.7% and 76.2%, respectively). Although the univariate analysis showed that the asymptomatic joints had smaller mean fossa width to fossa depth ratios (P<.0005), shorter mean eminence length (P<.007), and more concentric to anterior mean condyle position (P<.049), overlap in most of the ranges limited the predictive value. CONCLUSION: Within the limitations of this study, multifactorial analysis revealed that several subsets of asymptomatic temporomandibular joints could be distinguished from joints with disk displacement according to hard tissue measurements taken from central section tomograms. In general, asymptomatic normal joints were typified by interactions of less extreme ranges of fossa size, shape, and condyle position.