The 2018 classification of periodontal diseases: its predictive value for tooth loss
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 patient...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2022
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| In: |
Journal of periodontology
Year: 2022, Jahrgang: 93, Heft: 4, Pages: 560-569 |
| ISSN: | 1943-3670 |
| DOI: | 10.1002/JPER.21-0211 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/JPER.21-0211 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/JPER.21-0211 
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| Verfasserangaben: | Nihad El Sayed, Sonja Rahim-Wöstefeld, Florian Stocker, Rouven Behnisch, Peter Eickholz, Bernadette Pretzl |
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| 245 | 1 | 4 | |a The 2018 classification of periodontal diseases |b its predictive value for tooth loss |c Nihad El Sayed, Sonja Rahim-Wöstefeld, Florian Stocker, Rouven Behnisch, Peter Eickholz, Bernadette Pretzl |
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| 520 | |a 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. | ||
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