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

• Published in: BMC oral health Vol. 24; no. 1; pp. 1047 - 14
• Main Authors: Cui, Yuchen, Kang, Fujia, Li, Xinpeng, Shi, Xinning, Zhu, Xianchun
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.09.2024; BioMed Central; BMC
• Subjects: Adolescent; Adult; Algorithms; Analysis; Boruta algorithm; Care and treatment; China - epidemiology; College students; Diagnosis; Disease; Female; Generalized anxiety disorder; Health aspects; Humans; LASSO regression; Machine learning; Male; Medical history; Mental depression; Methods; Mouth; Neck; Nomogram; Nomograms; Nomography (Mathematics); Pain; Patients; Prediction models; Prevalence; Public health; Questionnaires; Risk Assessment; Risk Factors; Social aspects; Students - statistics & numerical data; Teeth; Temporomandibular disorders; Temporomandibular joint; Temporomandibular joint disorders; Temporomandibular Joint Disorders - epidemiology; Universities; University students; Validity; Young Adult; China; LASSO regression; Temporomandibular disorders; Boruta algorithm; Nomogram; Risk factors
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-024-04832-3

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. 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. 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. 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.