Journal of periodontology · 2026
Development of simple predictive models of tooth loss in the community setting.
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Furuta Michiko, Honda Takanori, Kageyama Shinya, Sawada Nanami, Sakata Satoko, Hata Jun, Ninomiya Toshiharu, Takeshita Toru
Аннотация
BACKGROUND: A simple tool for predicting the risk of tooth loss is considered valuable for the implementation of early preventive measures in community settings. This study aimed to establish a simple predictive model for tooth loss in a community-dwelling Japanese population.
METHODS: This longitudinal study included 1755 participants aged 40-79 years who underwent dental examinations between 2007 and 2012. Incident tooth loss was defined as losing ≥2 teeth over a 5-year period, corresponding to the highest quartile of the number of teeth lost. Logistic regression analysis was employed for developing multivariate prediction models with and without dental examination data. The developed models were translated into simplified scoring systems based on the beta coefficients. The discrimination of the model was assessed using the Hosmer-Lemeshow test, and the calibration was assessed using a calibration plot.
RESULTS: The incidence of tooth loss was 24.1%. The multivariable model without dental examination data, which included age, current smoking, diabetes, periodontal treatment, number of teeth present, and occupational status as predictors, demonstrated good discrimination and calibration. The multivariable model with dental examination parameters (periodontitis stage and untreated decayed teeth) as predictors demonstrated better predictive performance. The simplified scores, which were developed based on multivariable models, demonstrated predictive performances comparable to that of their respective multivariable models.
CONCLUSION: The prediction model, even the model without dental examination data can be used to identify individuals at high risk for tooth loss. The simple scoring tool for predicting tooth loss can be readily implemented in the community.
PLAIN LANGUAGE SUMMARY: We developed a multivariable risk prediction model and simplified scoring system for predicting tooth loss, which may help in the identification of individuals at a high risk of tooth loss in a community.
METHODS: This longitudinal study included 1755 participants aged 40-79 years who underwent dental examinations between 2007 and 2012. Incident tooth loss was defined as losing ≥2 teeth over a 5-year period, corresponding to the highest quartile of the number of teeth lost. Logistic regression analysis was employed for developing multivariate prediction models with and without dental examination data. The developed models were translated into simplified scoring systems based on the beta coefficients. The discrimination of the model was assessed using the Hosmer-Lemeshow test, and the calibration was assessed using a calibration plot.
RESULTS: The incidence of tooth loss was 24.1%. The multivariable model without dental examination data, which included age, current smoking, diabetes, periodontal treatment, number of teeth present, and occupational status as predictors, demonstrated good discrimination and calibration. The multivariable model with dental examination parameters (periodontitis stage and untreated decayed teeth) as predictors demonstrated better predictive performance. The simplified scores, which were developed based on multivariable models, demonstrated predictive performances comparable to that of their respective multivariable models.
CONCLUSION: The prediction model, even the model without dental examination data can be used to identify individuals at high risk for tooth loss. The simple scoring tool for predicting tooth loss can be readily implemented in the community.
PLAIN LANGUAGE SUMMARY: We developed a multivariable risk prediction model and simplified scoring system for predicting tooth loss, which may help in the identification of individuals at a high risk of tooth loss in a community.