Prevalence and Predictive Factors for Peri-Implant Disease and Implant Failure: A Cross-Sectional Analysis

• Diane M. Daubert ^[1] ; Bradley F. Weinstein ^[1] ; Sandra Bordin ^[1] ; Brian G. Leroux ^[1] ; Thomas F. Flemmig ^[1]
  1. [1] University of Washington

     University of Washington

     Estados Unidos

     
• Localización: Journal of periodontology, ISSN 0022-3492, Vol. 86, Nº. 3, 2015, págs. 337-347
• Idioma: inglés
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• Resumen

  • Background: Long-term studies worldwide indicate that peri-implant inflammation is a frequent finding and that the prevalence of peri-implantitis correlates with loading time. Implant loss, although less frequent, has serious oral health and economic consequences. An understanding of predictive factors for peri-implant disease and implant loss would help providers and patients make informed decisions.

    Methods: A cross-sectional study was performed on 96 patients with 225 implants that were placed between 1998 and 2003. Implant placement data were collected from patient records, and patients presented for a clinical and radiographic follow-up examination. Implant status and periodontal status were determined, the data were analyzed to determine the prevalence of peri-implant disease or implant loss, and a predictive model was tested.

    Results: The mean follow-up time for the patients was 10.9 years. The implant survival rate was 91.6%. Peri-implant mucositis was found in 33% of the implants and 48% of the patients, and peri-implantitis occurred in 16% of the implants and 26% of the patients. Individuals with peri-implantitis were twice as likely to report a problem with an implant as individuals with healthy implants. Peri-implantitis is associated with younger ages and diabetes at the time of placement and with periodontal status at the time of follow-up. Implant loss is associated with diabetes, immediate placement, and larger-diameter implants.

    Conclusions: One in four patients and one in six implants have peri-implantitis after 11 years. The data suggest that periodontal and diabetes status of the patient may be useful for predicting implant outcomes.