Periodontitis is becoming increasingly common in youth. However, a validated model for assessing the risk of periodontitis in youth is lacking. This study aimed to identify independent risk factors and develop a nomogram for assessing periodontitis risk in youth within a clinical setting. In this cross-sectional study, we recruited 807 participants aged 10–24 from the Periodontology Department of Qingdao Stomatological Hospital (Qingdao, Shandong, China). Data on 16 potential risk factors were collected. The participants were randomly allocated to the training and validation cohorts in a 7:3 ratio. Independent factors were identified via univariate and multivariate logistic regression analysis, which were then used to develop a nomogram for assessing the risk of periodontitis in youth within this clinical setting. The model’s validation and performance were rigorously evaluated using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), calibration plots, and decision curve analysis (DCA). Multivariate logistic regression analysis identified age, mouth breathing, impacted teeth, family history, and higher body mass index (BMI) as significant risk factors of periodontitis, while flossing was a protective factor. Accordingly, a nomogram was developed based on these factors as a risk assessing tool for youth in a clinical setting. Both the training and validation cohorts confirmed the nomogram’s strong assessment performance, evidenced by AUC of 0.837 and 0.783, respectively. The model also maintained high sensitivity (91.2% and 86.5%) and NPV (91.5% and 90.8%) across both cohorts. Good calibration was achieved, with Brier scores of 0.162 (95% CI: 0.145–0.179) for the training cohort and 0.177 (95% CI: 0.148–0.207) for the validation cohort. Furthermore, the model’s clinical utility was underscored by the DCA. We successfully developed and validated the nomogram for assessing the risk of periodontitis in youth within a clinical setting. With its high AUC, this model shows significant potential to support clinical decision-making by identifying high-risk individuals, thereby facilitating targeted evaluation and optimized management in specialist practice.
Zhang et al. (Sat,) studied this question.