Prediction of Failure of Dental Implants

Academic Centre for Dentistry in Amsterdam

Status

Completed

Conditions

Implant Complication

Peri-Implantitis

Treatments

Procedure: Placement of dental implants

Study type

Observational

Funder types

Other

Identifiers

NCT04129957

2018.376

Details and patient eligibility

About

The aim of the study is to identify predictors in patient profiles and implant characteristics and to develop and calibrate a prediction model for failure of implants. Patients' demographic characteristics, lifestyle habits, general health, dental health, and implant characteristics were regarded as potential predictors. The failure of implants and the follow-up time in days of implants were considered the outcome. Multivariate Cox proportional hazards regression analysis is used to find out the important risk factors for failure of dental implants and to develop the model for prediction of failure of dental implants at follow-up. The performance and clinical values of the model is determined.

Full description

During the past decades, dental implant therapy has developed into a successful treatment option for patients confronted with both partial and complete edentulism. Based on the literature, the survival rate of dental implants, which is defined that the dental implants are still in the mouth after insertion, is around 95% in the 5-year follow-up and around 90% in the 10-year follow-up. The success rate of dental implants, which is defined as dental implants in function, with good hard and soft tissue physiology and user satisfaction ranges from 85.2% to 88.7% in the follow-up of up to 20 years. This indicates that both the success rate and survival rate of dental implants is high. However, both survival rates and success rates vary across patients with different profiles. The expense of dental implant treatment is high and implant placement is a surgical procedure which is invasive and thus risky for the patients' health. Once the failure of dental implants occurs, it may cause some severe negative consequences for patients. For example, the failure will cause a financial loss for patients and a possible shock concerning both mental and physical aspects. To reduce these risks it is important and necessary for clinicians to be able to predict the risk of the failure of dental implants of individual patients before they undergo dental implant treatment.

Aim:

The aim of the project is to find out the possible risk factors for failure of dental implants and to develop a prediction model for the failure of dental implants at follow-up as a tool for clinicians to establish patients individual risk profile.

Methods:

The study is a retrospective design. The clinical data of the adult patients who were referred to the Department of Oral Implantology and Prosthetic Dentistry, Academic Centre for Dentistry Amsterdam (ACTA) for placement of dental implants from September 2009 to September 2013 are collected retrospectively from the clinical data management system of ACTA in the study.

The potential predictors include five domains: patients' demographic characteristics, lifestyle habits, general health, dental health, and implant characteristics. These predictors are pre-screened by international experts in dental implantology based their clinical knowledge and experience.

The outcomes included the follow-up time of implants and whether the failure of the implants was observed at the follow-up. The follow-up time is defined as the difference in time between implant placement and implant failure, or the date of the last follow-up time point if the dental implant is in an acceptable state. The failure of implants is defined as the presence of peri-implantitis, presence of mobility of implants, or removal of the implants for any reasons, for instance, unacceptable performance in aspects of function, tissue physiology, esthetics, and patients' satisfaction after placement of suprastructure.

The Multivariate Cox proportional hazards regression analysis will be used to find out the important risk factors and to develop the model. The performance of the model, in aspects of calibration and discrimination, is assessed. The clinical added values of the model is assessed. Then, the model is transformed into a score chart and a line chart, which is easy-to-use to the clinicians for the prediction.

Enrollment

337 patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

patients were over 18 years old at baseline;
patients underwent the placement of at least one implant;
patients were followed up for the implants at least one time after placement of implants;
patients provided their informed consent.

Exclusion criteria