Immediate Loading of Splinted Locking-Taper Implants: 1-Year Survival Estimates and Risk Factors for Failure

Purpose: The purpose of this study was to estimate the 1-year survival rate of immediate vertical-load splinted locking-taper implants and to identify risk factors for implant failure. Materials and Methods: To address the research aim, the investigators implemented a retrospective cohort study design and enrolled a sample derived from the population of patients who had received immediate vertical-load splinted implants (Bicon, Boston, MA). The predictor variables were the sets of exposures associated with implant failure and classified as demographic, health status, anatomic, implant specific, prosthetic, and surgical. The primary outcome variable was implant failure, which was defined as implant removal. Descriptive, univariate, and multivariate analyses using clustered marginal approach of the Cox proportional hazards models were computed. The level of statistical significance was set at P .05. Results: The study cohort was composed of 209 patients who received 477 implants. The overall 1-year Kaplan-Meier survival estimate was 90.3% (95% CI: 86.9%, 93.7%). In the multivariate model, implant placement in a delayed manner versus implantation the same day as extraction (hazard ratio = 3.7, P = .002), uncoated implants versus coated implants (hazard ratio = 22.1, P .001), and an increased per-unit number of pontics involved in the temporary prosthesis (hazard ratio = 1.8, P .001) were statistically associated with an increased risk of implant failure. Conclusions: An overall 1-year survival estimate of 90.3% (95% CI: 86.9%, 93.7%) was calculated for immediately loaded splinted implants. After controlling for other variables, 3 variables-timing of implant placement relative to extraction (delayed implant placement after tooth extraction), coating of implant (uncoated), and increased number of pontics-were associated with an increased risk for implant failure. Palabras clave: dental implants, immediate loading, multivariate models, retrospective cohort study, risk factors