Risk Factors for Tooth Loss in Patients with ≥25 Remaining Teeth Undergoing Mid-Long-Term Maintenance : A Retrospective Study

• Kawahara, Hiroo

• Inoue, Miho

• Okura, Kazuo

• Oshima, Masamitsu

• Matsuka, Yoshizo

抄録

Tooth loss represents a diffused pathologic condition affecting the worldwide population. Risk factors have been identified in both general features (smoking, diabetes, economic status) and local tooth-related factors (caries, periodontitis). In this retrospective study, we examined the data of 366 patients with a large number of remaining teeth (≥25) undergoing maintenance therapy in order to identify specific risk factors for tooth loss. The number of remaining teeth, number of non-vital teeth, and number of occlusal units were investigated for their correlation with tooth loss. The mean follow-up of patients was 9.2 years (range 5 to 14). Statistically significant risk factors for tooth loss were identified as number of remaining teeth at baseline (p = 0.05), number of occlusal units (p = 0.03), and number of non-vital teeth in posterior regions (p < 0.001). Multiple logistic regression showed that the number of occlusal units and number of non-vital teeth in the posterior regions were significantly associated with a greater risk of tooth loss (odds ratio 1.88 and 3.17, respectively). These results confirm that not only the number of remaining teeth, but also their vital or non-vital status and the distribution between the anterior and posterior regions influence the long-term survival.

収録刊行物

• International Journal of Environmental Research and Public Health

  International Journal of Environmental Research and Public Health 18 (13), 7174-, 2021-07-05

  MDPI

キーワード

• tooth loss
• risk factors
• occlusal units
• non-vital teeth
• remaining teeth
• posterior load

詳細情報

• CRID: 1050851862091047424
• NII論文ID: 120007125053
• ISSN: 16604601
• DOI: 10.3390/ijerph18137174
• Web Site: http://repo.lib.tokushima-u.ac.jp/116124
• Web Site: https://www.mdpi.com/1660-4601/18/13/7174/pdf
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN