Correlation of patients' demographics and clinical symptoms with temporomandibular disorders

The "5Ts" screening tool: Enhancements and threshold values for effective TMD identification

OBJECTIVE

This study aimed to enhance the quintessential "five temporomandibular disorder (TMD) symptoms" (5Ts) screener by incorporating frequency options and distinguishing between TMJ and muscle pain. The diagnostic accuracy along with cut-off points for the effective identification of TMDs was also established.

METHODS

Participants, aged ≥18 years, were recruited from a university-based hospital. After completing surveys encompassing demographic data and the enhanced 5Ts (with frequency options [5Ts-F] and differentiation of TMJ/muscle pain [6Ts-F]), protocolized interviews and clinical examinations were performed following DC/TMD. The diagnostic accuracy and best cut-off points were determined with the area under the receiver operating characteristic curves (AUCs).

RESULTS

324 participants were recruited (mean age 30.0 ± 11.4 years). Among these, 86.4% had TMDs. 5Ts exhibited high diagnostic accuracy for detecting all TMDs (AUC = 0.92) with sensitivity/specificity values of 83.9%/88.6%. Both 5Ts-F and 6Ts-F had slightly better accuracy (AUCs = 0.95/0.96), comparable sensitivity, and superior specificity (97.7%) compared to 5Ts. The best cut-off points were 1.5 for 5Ts and 2.5 for 5Ts-F/6Ts-F.

CONCLUSIONS

Although all three TMD screeners presented high diagnostic accuracy, 5Ts-F/6Ts-F had notably improved specificity. 5Ts scores of >1.5 and 5Ts-F/6Ts-F scores of >2.5 are to be applied for screening the presence of TMDs.

A nomogram for predicting the risk of temporomandibular disorders in university students

OBJECTIVES

Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model.

METHODS

This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University.

RESULTS

The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility.

CONCLUSIONS

An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients.

Correlation of clinical manifestations and condylar morphology of patients with temporomandibular degenerative joint diseases

OBJECTIVE

To study the correlation between condylar morphology and clinical manifestations in patients with degenerative joint disease (DJD) of the temporomandibular joint (TMJ).

METHODS

A total of 175 joints of 131 patients with DJD were included. Data on patients' basic information and symptoms were collected and analyzed. Condylar morphology was evaluated using cone beam computed tomography (CBCT). The correlation between the condylar morphology and clinical manifestations was analyzed.

RESULTS

The prevalence of joint noises, clicks, and crepitus was 93/175 (53%), 73/175 (42%), and 20/175 (11%), respectively. Condylar anteroposterior diameter and condylar height were correlated with pain. There was a correlation between the shape of the condyle in the sagittal plane and joint noise.

CONCLUSION

Condylar morphology and clinical features of DJD were correlated to some extent.

Prevalence and associated factors of health anxiety in patients with temporomandibular disorders

OBJECTIVES

To investigate the prevalence and associated factors of health anxiety (HA) in patients with Temporomandibular Disorders (TMDs) using the 8-item Whiteley Index (WI-8) scale.

MATERIALS AND METHODS

Three hundred and twenty-nine TMDs patients completed the Visual Analog Scale (VAS), WI-8, Jaw Functional Limitation Scale-8 (JFLS-8), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7) scales. Clinical examinations were conducted following the Diagnostic Criteria for TMDs Axis I.

RESULTS

The prevalence of HA among TMDs patients was 18.54%. Patients with HA had higher scores of VAS-current (p = 0.026), VAS-maximum (p = 0.024), VAS-average (p = 0.030), JFLS-8 (p < 0.001), GAD-7 (p < 0.001) and PHQ-9 (p < 0.001), lower maximum mouth opening (p = 0.016), lower proportion of structure-related TMDs (p = 0.028), and higher proportion of pain-related TMDs (p < 0.001) compared to those without HA. The correlation coefficient was 0.61 (p < 0.001) between WI-8 and GAD-7 and 0.64 (p < 0.001) between WI-8 and PHQ-9.

CONCLUSION

Approximately one-fifth of patients with TMDs experienced HA. HA was associated with pain perception, functional limitations, depressive, and anxiety symptoms in individuals with TMDs. HA may contribute to heightened subjective pain experiences rather than structural changes in the TMJ.

Temporomandibular joint synovial chondromatosis: An analysis of 7 cases and literature review

OBJECTIVE

To investigate the diagnosis and treatment procedure of synovial chondromatosis (SC) of the temporomandibular joint (TMJ).

METHODS

Clinical features, imaging features, surgical methods, and prognosis of 7 patients with SC of the TMJ were analyzed. We also reviewed and analyzed surgery-relevant literature included in the Pubmed database in the past decade using the search terms "synovial chondromatosis" and "temporomandibular joint", and found 181 cases.

RESULTS

There was no specific difference in the symptoms of SC in the TMJ in different Milgram's stages in our cases and the cases mentioned in the literature. The main symptoms of SC in the TMJ were pain (100%, 7/7; 64.64%, 117/181), limited mouth opening (57.14%, 4/7; 53.59%, 97/181), swelling (14.29%, 1/7; 28.18%, 51/181), crepitus (28.57%, 2/7; 19.34%, 35/181), and clicking (14.29%, 1/7; 9.94%, 18/181) in our cases and cases from literature separately. The imaging features of SC were occupying lesions (including loose bodies or masses) (71.42%, 5/7; 37.57%, 68/181), bone change in condyle or glenoid fossa (1/7, 14.29%; 34.81%, 63/181), effusion (42.86%, 3/7; 20.99%, 38/181), joint space changes (42.86%, 3/7; 11.05%, 20/181) in our cases and cases from literature separately. The surgical procedures seem to depend mainly on the involved structures and the extension of the lesion rather than the Milgram's stage.

CONCLUSIONS

The clinical features of SC in the TMJ are nonspecific and easy to be misdiagnosed. MRI is helpful in the diagnosis of SC in the TMJ. The surgical procedures mainly depend on the involved structures and the extension of the lesion.