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Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy.
Rahim-Wöstefeld, Sonja; Kronsteiner, Dorothea; ElSayed, Shirin; ElSayed, Nihad; Eickholz, Peter; Pretzl, Bernadette.
Afiliação
  • Rahim-Wöstefeld S; Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany. sonja.rahim@gmx.de.
  • Kronsteiner D; Private Practice, 68159, Mannheim, Germany. sonja.rahim@gmx.de.
  • ElSayed S; Institute of Medical Biometry and Informatics (IMBI), University Hospital Heidelberg, 69120, Heidelberg, Germany.
  • ElSayed N; Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany.
  • Eickholz P; Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany.
  • Pretzl B; Department of Periodontology, Center of Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, 60596, Frankfurt, Germany.
Clin Oral Investig ; 26(1): 813-822, 2022 Jan.
Article em En | MEDLINE | ID: mdl-34435251
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perda de Dente / Defeitos da Furca Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perda de Dente / Defeitos da Furca Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article