Development and validation of a dynamic nomogram for dental caries in Chinese children

Background: This study aimed to identify factors associated with dental caries in Chinese children and develop a validated risk prediction nomogram for this condition. Methods: A total of 450 pediatric outpatients from the Stomatology Department of the General Hospital of Northern Theater Command were enrolled and stratified into a caries group (n = 340) and a non-caries group (n = 110) based on clinical caries status. Univariable and multivariable logistic regression analyses, combined with Least Absolute Shrinkage and Selection Operator (LASSO) regression, were applied to screen independent predictors of dental caries, and a nomogram model was constructed based on the identified key factors, with internal validation using the original dataset. The discriminative power was measured by the receiver operating characteristic (ROC) curve’s area under the curve (AUC), whereas calibration was assessed using calibration plots and the Hosmer-Lemeshow test. Clinical applicability was examined through decision curve analysis (DCA) and clinical impact curves (CIC). Results: The following three factors contribute to the occurrence of dental caries: liking to eat snacks (p < 0.001), Undesirable Behavioral Habits (p = 0.039), and brushing teeth for less than 2 minutes (p < 0.001). The nomogram showed excellent discrimination, with an AUC of 0.939 (95% Confidence Interval: 0.911–0.967) confirmed by bootstrapping, and good calibration (Hosmer-Lemeshow test, p = 0.922). DCA indicated that using the model to guide targeted interventions provided superior net benefit across a wide probability threshold range (0.02–0.98), compared to strategies of intervening in all or no children. CIC displayed good predictive ability and clinical application value for the model. Conclusions: Prediction nomograms identified frequent snacking, undesirable behavioral habits, and brushing time <2 minutes as key predictors of childhood dental caries. Both clinical and online dynamic nomograms can screen high-risk children, enabling dentists to deliver individualized management and timely, effective interventions for them.

Dental caries; Nomogram; Prediction model; Risk factors

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