Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study

• Published in: Medicine (Baltimore) 2024-04, Vol.103 (14), p.e37635-e37635
• Main Authors: Zhang, Jimin, Huang, Zewen, Wang, Wei, Zhang, Lejun, Lu, Heli
• Format: Article
• Language: English
• Subjects: Observational Study
• ISSN: 0025-7974; 1536-5964
• DOI: 10.1097/MD.0000000000037635

Depressive symptoms are frequently occur among dentistry patients, many of whom struggle with dental anxiety and poor oral conditions. Identifying the factors that influence these symptoms can enable dentists to recognize and address mental health concerns more effectively. This study aimed to investigate the factors associated with depressive symptoms in dentistry patients and develop a clinical tool, a nomogram, to assist dentists in predicting these symptoms. Methods: After exclusion of ineligible participants, a total of 1355 patients from the dentistry department were included. The patients were randomly assigned to training and validation sets at a 2:1 ratio. The LASSO regression method was initially employed to select highly influrtial features. This was followed by the application of a multi-factor logistic regression to determine independent factors and construct a nomogram. And it was evaluated by 4 methods and 2 indicators. The nomograms were formulated based on questionnaire data collected from dentistry patients. Nomogram2 incorporated factors such as medical burden, personality traits (extraversion, conscientiousness, and emotional stability), life purpose, and life satisfaction. In the training set, Nomogram2 exhibited a Concordance index (C-index) of 0.805 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.805 (95% CI: 0.775-0.835). In the validation set, Nomogram2 demonstrated an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.810 (0.768-0.851) and a Concordance index (C-index) of 0.810. Similarly, Nomogram1 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.816 (0.788-0.845) and a Concordance index (C-index) of 0.816 in the training set, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.824 (95% CI: 0.784-0.864) and a Concordance index (C-index) of 0.824 in the validation set. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indicated that Nomogram1, which included oral-related factors (oral health and dental anxiety), outperformed Nomogram2. We developed a nomogram to predict depressive symptoms in dentistry patients. Importantly, this nomogram can serve as a valuable psychometric tool for dentists, facilitating the assessment of their patients' mental health and enabling more tailored treatment plans.