Aim To assess factors contributing to tooth loss 20 years after active periodontal therapy (APT) on tooth level. Materials and Methods After an initial retrospective analysis 10 years after APT, patients were monitored for 10 more years. At clinical re‐evaluation 20 years after APT, tooth‐related factors (tooth type, location, bone loss, furcation involvement, abutment status) and patient‐related factors (gender, smoking, adherence) were investigated. Descriptive statistical analysis and a mixed logistic regression analysis were performed with tooth loss as primary outcome variable. Results The study included 69 patients (42 female/27 male). 39 patients were non‐adherent (56.5%), and 11 were active smokers (15.9%). A total of 198 out of 1611 teeth were lost. Tooth loss was significantly highest (p < .01) in molars (21.1%), multi‐rooted teeth with furcation involvement (23.5%) and abutment teeth (fixed: 27.6%, removable: 36.4%). 37.6% of teeth with initial bone loss >60% were lost during 20 years. Adherent patients showed less frequent tooth loss than non‐adherent patients (OR 0.371; p <  .01). Conclusion Even teeth with an initial bone loss over 60% could be retained in approximately two thirds for 20 years. This should be kept in mind when assigning prognosis and establishing a treatment plan.

Background Predictive models and assessment tools for disease susceptibility and progression are necessary to enhance personalized medicine. The aim of this study is to assess the predictive accuracy of using the 2018 classification to predict likelihood of tooth loss. Methods A total of 134 patients were screened 10 years after periodontal therapy. Data were extracted from 82 patients’ records and periodontal diagnoses were assigned according to the 1999 and 2018 classifications at baseline, whereas patient‐ and tooth‐related parameters were documented at baseline and at reexamination. Statistical analysis included descriptive statistics, hurdle regression with a zero and count model as well as logistic regression. Results Significantly more teeth were lost during SPT in patients with Stage IV or Grade C (P < 0.05). Patients’ adherence seems to have an impact on the predictability of the 2018 classification (P < 0.001). In comparison, neither classification system alone (1999 vs 2018) showed a high predictive value for tooth loss (area under the curve [AUC] = 59.2% vs 58.2%). Conclusion Class III and IV/Grade C of the 2018 classification of periodontal diseases show similar predictive accuracy for tooth loss as severe cases in the former classification. Patients adherence seems to influence the prognostic value of the classification.

Objectives The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.