Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

Abstract

Abstract Background Tic disorders (TD) are complex neuropsychiatric disorders frequently associated with a variety of comorbid problems, whose negative effects may exceed those of the tics themselves. In this study, we aimed to explore the sociodemographic and clinical characteristics of children with TD and behavioral problems, and develop a prediction model of behavioral problems based on the predictors under real-world conditions. Methods A hospital-based cross-sectional study was conducted on children with TD. Behavioral problems were surveyed using the Achenbach Child Behavior Checklist (CBCL). Sociodemographic information was collected from face-to-face interviews using an electronic questionnaire administered during the initial ambulatory visit. Clinical data were collected from medical records, and quality control was performed. The sociodemographic and clinical characteristics of patients with and without behavioral problems were statistically compared, and a nomogram prediction model was developed based on multivariate logistic regression analysis. The discriminatory ability and clinical utility of the nomogram were assessed by concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC). Results A total of 343 TD cases were included in the final analysis, of which 30.32% had behavioral problems. The prediction model showed age 12–16 years, abnormal birth history, parenting pattern of indulgence, parent/close relatives with psychiatric disorders, chronic motor or vocal tic disorder (CTD)/Tourette syndrome (TS) and moderate/severe tic severity were associated with behavioral problems in children with TD. The C-index of the prediction model (nomogram) was 0.763 (95% confidence interval, 0.710 ~ 0.816). The nomogram was feasible for making beneficial clinical decisions, according to the satisfactory results of the DCA and CIC. Conclusions A nomogram prediction model for comorbid behavioral problems in children with TD was established. The prediction model demonstrated a good discriminative ability and predictive performance for beneficial clinical decisions. This model further provides a comprehensive understanding of associated sociodemographic and clinical characteristics by visual graphs and allows clinicians to rapidly identify patients with a higher risk of behavioral problems and tailor necessary interventions to improve clinical outcomes.