Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study

The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18–50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41–2.93)], anemia [OR = 1.69 (1.48–1.93)], and age [OR = 1.07 (1.06–1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients

Pre-Operative Oral Health-Related Quality of Life in Patients Attending Surgical Removal of Mandibular Third Molar Teeth

The study aimed to measure the pre-operative oral health-related quality of life (OHRQoL) and to identify patient and teeth pathologies associated with worse OHRQoL among patients attending mandibular third molar tooth extraction. Data were collected preoperatively from 199 patients attending surgical removal of their mandibular third molar. To that end, we measured the Oral Health Impact Profile-14 (OHIP-14) and analyzed its association with: (1) demographics; (2) health-related behaviors such as smoking, alcohol consumption, physical activity, and dietary habits; (3) Plaque Index (PI); (4) Decay, Missing, and Filled Teeth (DMFT); and (5) clinical characteristics related to third molar extraction, such as the indication for extraction, tooth angulations, and radiographic pathology. The mean age of the study population was 21.5 ± 3.2 years and the mean OHIP-14 global score was 22.5 ± 8.3. The present study identified patient and teeth profiles that are associated with worse pre-operative OHRQoL in patients attending mandibular third molar extraction. The “vulnerable patient” profile includes poor health-related behaviors, particularly the performance of physical activity less than once a week (p = 0.028). The “disturbing teeth” profile includes higher plaque scores (p = 0.023) and specific characteristics of the third molar teeth, such as pericoronitis (p = 0.027) and radiolucency around third molars in panoramic radiography (p < 0.001). These findings support the hypothesis that OHRQoL is a complex phenomenon which is associated with the patient’s health-related behaviors as well as with specific tooth pathologies

Patterns of Cone-Beam Computed Tomography (CBCT) Utilization by Various Dental Specialties: A 4-Year Retrospective Analysis from a Dental and Maxillofacial Specialty Center

The study aimed to analyze the uses of cone-beam computed tomography (CBCT) in the diagnosis and treatment in various dental specialties. This 4-year cross-sectional study analyzed the records of 1409 individuals who underwent a CBCT at the Oral and Maxillofacial Center at Sheba Medical Center, Israel. The average age of the patients was 27.9 ± 11.5 (range: 9–86 years). Patients were referred for CBCT by the following departments: Oral and Maxillofacial Surgery (1063; 75.5%), Endodontics (182; 12.9%), Periodontology (122; 8.6%) and Orthodontics (42; 3.0%). Most CBCT radiographs evaluated the maxilla (774; 55.0%), followed by the mandible (481; 34.1%) and both (154; 10.9%). The target anatomical structures included: bone (694; 49.3%), teeth (307; 21.7%), and both jaws (408; 29.0%). The main indications for CBCT use were: assessment of anatomical structures and implant sites (787; 55.9%), determine root canals morphology (182; 12.9%), visualization of impacted teeth, tooth alignment, and localization (177; 12.6%), suspected cysts or tumors (148; 10.5%), evaluation of Temporomandibular joint disorders (106; 7.5%) and other reasons (9; 0.6%). In 279 (19.8%) of cases, the diagnosis changed following CBCT, mainly in Orthodontics tooth analysis (28 (66.7%); p < 0.001). Practitioners and health authorities should be aware of this baseline information regarding CBCT use in the diagnosis and assessment of various oral and maxillofacial pathologies, anomalies and tooth position relative to anatomic structures. Continuing research and publications of CBCT utilization and guidelines are recommended