Σάββατο 19 Ιανουαρίου 2019

Predictive factors for tooth loss during supportive periodontal therapy in patients with severe periodontitis: a Japanese multicenter study

Abstract

Background

Supportive periodontal therapy (SPT) must take individual patient risk factors into account. We conducted a multicenter joint retrospective cohort study to investigate the value of modified periodontal risk assessment (MPRA) and therapy-resistant periodontitis (TRP) assessment as predictive factors for tooth loss due to periodontal disease in patients with severe periodontitis during SPT.

Methods

The subjects were 82 patients from 11 dental institutions who were diagnosed with severe periodontitis and continued SPT for at least 1 year (mean follow-up = 4.9 years) between 1981 and 2008. The outcome was tooth loss due to periodontal disease during SPT. The Cox proportional hazards model was used to analyze sex, age, diabetes status, smoking history, number of periodontal pockets measuring ≥6 mm, rate of bleeding on probing, bone loss/age ratio, number of teeth lost, MPRA, and TRP assessment as explanatory variables.

Results

Univariate analysis showed that loss of ≥8 teeth by the start of SPT [hazard ratio (HR) 2.86], MPRA score indicating moderate risk (HR 8.73) or high risk (HR 11.04), and TRP assessment as poor responsiveness to treatment (HR 2.79) were significantly associated with tooth loss (p < 0.05). In a model in which the explanatory variables of an association that was statistically significant were added simultaneously, the HR for poor responsiveness to treatment and ≥8 teeth lost was significant at 20.17 compared with patients whose TRP assessment indicated that they responded favorably to treatment and who had lost <8 teeth by the start of SPT.

Conclusion

MPRA and TRP assessment may be useful predictive factors for tooth loss due to periodontal disease during SPT in Japanese patients with severe periodontitis. Additionally, considering the number of teeth lost by the start of SPT in TRP assessment may improve its predictive accuracy.



from #Head and Neck by Sfakianakis via simeraentaxei on Inoreader http://bit.ly/2HkgjXe

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