This study reviews the literature regarding the factors contributing to failures of dental implants. An electronic search was undertaken including papers from 2004 onwards. The titles and abstracts from these results were read to identify studies within the selection criteria. All reference lists of the selected studies were then hand-searched, this time without time restrictions. A narrative review discussed some findings from the first two parts where separate data from non-comparative studies may have indicated conclusions different from those possible to draw in the systematic analysis. It may be suggested that the following situations are correlated to increase the implant failure rate: a low insertion torque of implants that are planned to be immediately or early loaded, inexperienced surgeons inserting the implants, implant insertion in the maxilla, implant insertion in the posterior region of the jaws, implants in heavy smokers, implant insertion in bone qualities type III and IV, implant insertion in places with small bone volumes, use of shorter length implants, greater number of implants placed per patient, lack of initial implant stability, use of cylindrical (non-threaded) implants and prosthetic rehabilitation with implant-supported overdentures. Moreover, it may be suggested that the following situations may be correlated with an increase in the implant failure rate: use of the non-submerged technique, immediate loading, implant insertion in fresh extraction sockets, smaller diameter implants. Some recently published studies suggest that modern, moderately rough implants may present with similar results irrespective if placed in maxillas, in smoking patients or using only short implants.

OBJECTIVE: Recent studies implicate smoking as a significant factor in the failure of dental implants. This review aims to test the null hypothesis of no difference in the implant failure rates, risk of postoperative infection, and marginal bone loss for smokers versus non-smokers, against the alternative hypothesis of a difference. DATA: Main search terms used in combination: dental implant, oral implant, smoking, tobacco, nicotine, smoker, and non-smoker. SOURCES: An electronic search was undertaken in September/2014 in PubMed/Medline, Web of Science, Cochrane Oral Health Group Trials Register plus hand-searching. STUDY SELECTION: Eligibility criteria included clinical human studies, either randomized or not. The search strategy resulted in 1432 publications, of which 107 were eligible, with 19,836 implants placed in smokers, with 1259 failures (6.35%), and 60,464 implants placed in non-smokers, with 1923 failures (3.18%). CONCLUSIONS: The insertion of implants in smokers significantly affected the failure rates, the risk of postoperative infections as well as the marginal bone loss. The results should be interpreted with caution due to the presence of uncontrolled confounding factors in the included studies. CLINICAL SIGNIFICANCE: Smoking is a factor that has the potential to negatively affect healing and the outcome of implant treatment. It is important to perform an updated periodic review to synthesize the clinical research evidence relevant to the matter.

The purpose of the present study was to assess the influence of local and systemic factors on the occurrence of dental implant failures up to the second-stage surgery (abutment connection). This retrospective study is based on 2,670 patients who received 10,096 implants and were consecutively treated with implant-supported prostheses between 1980 and 2014 at 1 specialist clinic. Several anatomic-, patient-, health-, and implant-related factors were collected. Descriptive statistics were used to describe the patients and implants. Univariate and multivariate logistic regression models were used at the patient level as well as the implant level to evaluate the effect of explanatory variables on the failure of implants up to abutment connection. A generalized estimating equation method was used for the implant-level analysis to account for the fact that repeated observations (several implants) were available for a single patient. Overall, 642 implants (6.36%) failed, of which 176 (1.74%) in 139 patients were lost up to second-stage surgery. The distribution of implants in sites of different bone quantities and qualities was quite similar between implants lost up to and after abutment connection. Smoking and the intake of antidepressants were the statistically significant predictors in the multivariate model (ClinicalTrials.gov NCT02369562).

Objectives. To analyze the complications of dental implant treatment in a group of patients with bruxism in comparison with a matched group of non-bruxers. Material and methods. Patients being diagnosed as bruxers were identified within a group of patients consecutively treated with implant-supported prostheses at one specialist clinic, based on the most recent listed sign and symptoms of bruxism according to the International Classification of Sleep Disorders. A diagnostic grading system of “possible,” “probable,” and “definite” sleep or awake bruxism was used, according to a recent published international consensus. A case–control matching model was used to match the bruxers with a group of non-bruxers, based on five variables. Implant-, prosthetic-, and patient-related data were collected, as well as 14 mechanical complications, and compared between groups. Results. Ninety-eight of 2670 patients were identified as bruxers. The odds ratio of implant failure in bruxers in relation to non-bruxers was 2.71 (95% CI 1.25, 5.88). Considering the same number of patients with the same total number of implants equally distributed between groups, the bruxers group had a higher prevalence of mechanical complications in comparison with the non-bruxers group. Conclusions. This study suggests that bruxism may significantly increase both the implant failure rate and the rate of mechanical and technical complications of implant-supported restorations. Other risk factors may also have influenced the results.

Purpose: To investigate the association between the intake of proton pump inhibitors (PPIs) and the risk of dental implant failure. Materials and Methods: The present retrospective cohort study is based on patients consecutively treated between 1980 and 2014 with implant-supported/retained prostheses at one specialist clinic. Modern endosseous dental implants with cylindrical or conical design were included, and only complete cases were considered; ie, only those implants with information available for all variables measured were included in the regression model analysis. Zygomatic implants and implants detected in radiographies but without basic information about them in the patients’ files were excluded from the study. Implant- and patient-related data were collected. Multilevel mixed-effects parametric survival analysis was used to test the association between PPI exposure (predictor variable) and risk of implant failure (outcome variable), adjusting for several potential confounders. Results: A total of 3,559 implants were placed in 999 patients, with 178 implants reported as failures. The implant failure rates were 12.0% (30/250) for PPI users and 4.5% (148/3,309) for nonusers. A total of 45 out of 178 (25.3%) failed implants were lost up to abutment connection (6 in PPI users, 39 in nonusers), with an early-to-late failure ratio of 0.34:1. The intake of PPIs was shown to have a statistically significant negative effect for implant survival rate (HR 2.811; 95% CI: 1.139 to 6.937; P = .025). Bruxism, smoking, implant length, prophylactic antibiotic regimen, and implant location were also identified as factors with a statistically significant effect on the implant survival rate. Conclusion: This study suggests that the intake of PPIs may be associated with an increased risk of dental implant failure.

The aim of this retrospective study was to investigate the association between the intake of selective serotonin reuptake inhibitors (SSRIs) and the risk of dental implant failure. Patients were included if they were taking SSRIs only and no other medication, did not present any other systemic condition or compromising habits (bruxism, smoking, snuff), and complied with the use of prophylactic antibiotics for implant surgery. The multivariate generalized estimating equation (GEE) method and multilevel mixed-effects parametric survival analysis were used to test the association between SSRI exposure (predictor variable) and the risk of implant failure (outcome variable), adjusting for several potential confounders (other variables). The total number of implants with information available and meeting the necessary eligibility criteria was 931 (35 failures). These were placed in 300 patients. The implant failure rate was 12.5% for SSRI users and 3.3% for non-users (P = 0.007). Kaplan–Meier analysis showed a statistically significant difference in the cumulative survival rate (P < 0.001). The multivariate GEE model did not show a statistically significant association between SSRI intake and implant failure (P = 0.530), nor did the multilevel model (P = 0.125). It is suggested that the intake of SSRIs may not be associated with an increased risk of dental implant failure.

Purpose: To assess the influence of several factors on the prevalence of dental implant failure, with special consideration of the placement of implants by different dental surgeons. Materials and Methods: This retrospective study is based on 2,670 patients who received 10,096 implants at one specialist clinic. Only the data of patients and implants treated by surgeons who had inserted a minimum of 200 implants at the clinic were included. Kaplan-Meier curves were stratified with respect to the individual surgeon. A generalized estimating equation (GEE) method was used to account for the fact that repeated observations (several implants) were placed in a single patient. The factors bone quantity, bone quality, implant location, implant surface, and implant system were analyzed with descriptive statistics separately for each individual surgeon. Results: A total of 10 surgeons were eligible. The differences between the survival curves of each individual were statistically significant. The multivariate GEE model showed the following variables to be statistically significant: surgeon, bruxism, intake of antidepressants, location, implant length, and implant system. The surgeon with the highest absolute number of failures was also the one who inserted the most implants in sites of poor bone and used turned implants in most cases, whereas the surgeon with the lowest absolute number of failures used mainly modern implants. Separate survival analyses of turned and modern implants stratified for the individual surgeon showed statistically significant differences in cumulative survival. Conclusion: Different levels of failure incidence could be observed between the surgeons, occasionally reaching significant levels. Although a direct causal relationship could not be ascertained, the results of the present study suggest that the surgeons’ technique, skills, and/or judgment may negatively influence implant survival rates.

Background: Some studies indicated that implant failures are commonly concentrated in few patients. Purpose: To identify and analyze cluster behavior of dental implant failures among subjects of a retrospective study. Materials and Methods: This retrospective study included patients receiving at least three implants only. Patients presenting at least three implant failures were classified as presenting a cluster behavior. Univariate and multivariate logistic regression models and generalized estimating equations analysis evaluated the effect of explanatory variables on the cluster behavior. Results: There were 1406 patients with three or more implants (8337 implants, 592 failures). Sixty-seven (4.77%) patients presented cluster behavior, with 56.8% of all implant failures. The intake of antidepressants and bruxism were identified as potential negative factors exerting a statistically significant influence on a cluster behavior at the patient-level. The negative factors at the implant-level were turned implants, short implants, poor bone quality, age of the patient, the intake of medicaments to reduce the acid gastric production, smoking, and bruxism. Conclusions: A cluster pattern among patients with implant failure is highly probable. Factors of interest as predictors for implant failures could be a number of systemic and local factors, although a direct causal relationship cannot be ascertained.

Background: Very long-term follow-up of oral implants is seldom reported in the literature. Purpose: To assess oral implant failure rates and marginal bone loss (MBL) of patients followed up for a minimum of 20 years. Materials and Methods: Implants placed in patients followed up for 20+ years were included. Descriptive statistics, survival analyses, generalized estimating equations were performed. Three-hundred implants were randomly selected for MBL. Results: 1,045 implants (227 patients) were included. Implant location, irradiation, and bruxism affected the implant survival rate. Thirty-five percent of the failures occurred within the first year after implantation, and another 26.8% in the second/third year. There was a cumulative survival rate of 87.8% after 36 years of follow-up. In the last radiological follow up, 35 implants (11.7%) had bone gain, and 35 implants (11.7%) presented at least 3 mm of MBL. Twenty-six out of 86 failed implants with available radiograms presented severe MBL in the last radiological register before implant failure. Conclusions: Most of the implant failures occurred at the first few years after implantation, regardless of a very long follow up. MBL can be insignificant in long-term observations, but it may, nevertheless, be the cause of secondary failure of oral implants in some cases.