AI Article Synopsis

  • This study focused on creating and validating a model to predict the success or failure of dental implants affected by peri-implant disease, using data from 240 patients over one year.
  • Key factors influencing implant outcomes included age, a history of periodontitis, the severity of peri-implant disease, implant length, and the timing of disease onset, with relative risks calculated for each factor.
  • The developed risk model showed strong performance in predicting outcomes, as evidenced by good c-statistic values in both the initial model creation and subsequent validation phases.

Article Abstract

Background: This investigation, based on a 1-year retrospective cohort study, aimed to estimate and validate a prognostic model for ailing and failing implants due to peri-implant disease.

Methods: A total of 240 patients (male: 97; female: 143; average age of 57.3 years) with at least one ailing or failing implant were included: 120 patients for model derivation and 120 patients for model validation. The primary outcome measure was the implant status: success, defined as the arrest of the disease, or failure defined as implant extraction, prevalence or re-incidence of peri-implant disease). Potential prognostic risk indicators were collected at the baseline evaluation. The relative risk (RR) was estimated for the predictors through logistic regression and the c-statistic (95% confidence interval) was calculated for both derivation and validation sets. The significance level was set at 5%.

Results: The risk model retrieved the prognostic factors age (RR = 1.04), history of Periodontitis (RR = 3.13), severe peri-implant disease status (RR = 3.26), implant length (RR = 3.52), early disease development (RR = 3.99), with good discrimination in both the derivation set (0.763 [0.679; 0.847]) and validation set (0.709 [0.616; 0.803]).

Conclusions: A prognostic risk model for estimating the outcome of implants with peri-implant disease is available, with a good performance considering the c-statistic evaluation.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6780417PMC
http://dx.doi.org/10.3390/jcm8091352DOI Listing

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