Analysis of risk factors for early failure of simple taper retentive implants

Monte Carlo simulation of reasons for early failure of implants: effects of two risk factors

To report the prevalence of early implant failure and evaluate factors that contribute to the early failure of dental implants placed at a teaching clinic. The study also aims to identify risk indicators for early implant loss to better predict and prevent early implant loss in the future. This retrospective study included all patients with a dental implant placed by the Section of Oral Surgery and Oral Medicine, Department of Clinical Dentistry, University of Bergen, between January 2011 and December 2018. All information was collected from operation logbooks and from patient records. A failed implant in this study was defined as an implant lost before functional loading. A total of 1,005 dental implants were placed in the studied time period, of which 54 failed early, giving an early failure rate (EFR) of 5.4%, with functional loading obtained for the remaining 94.6%. Analysis showed an increased hazard for early implant failure among smokers, men, and younger patients. With an age increase of 10 years, the risk of implant failure was reduced by 14% (hazard ratio [HRR] = 0.86, P = .037). A higher failure rate was found in anterior maxillary implants than in posterior maxillary implants (7.79% vs 3.29%, respectively; HRR = 0.47; P = .041). The probability for early failure in the posterior mandible was significantly increased compared to the posterior maxilla (HRR = 3.68, P = .005). If the first implant failed, it was more likely that the consecutive implant would also fail (HRR = 1.82). In the study, 53.4% of the placed implants were Straumann (EFR = 5.2%), 30.3% were Nobel Biocare (EFR = 7.2%), and 16.3% were Astra Tech (EFR = 2.5%). This study found that younger, male, and smoker patients were associated with an increase in early failure of dental implants. Significantly increased failure rates were also seen for implants placed in the mandible, and there were differences with respect to implant system. Although differences were found in early failure both for patient- and implant-related factors, the overall early failure rate (5.4%) in this study was low.

AimThere are several causes related to early dental implant failure, and infection is one of the most common risk factors in the initial phase of osseointegration. The aim of this retrospective study was to evaluate early dental implant failure associated with postoperative infection and identify the factors associated with such failure.Materials and MethodsA retrospective study was conducted, and the main predictor variable was early dental implant failure. The main cause of failure was postoperative infection, including factors related to early loss due to infection, such as installation site, type of implant–abutment connection used and presence of previous bone graft. Appropriate descriptive and multivariate statistics were computed, and Chi‐square tests and logistic binary regression were used to identify the factors related to early failure.ResultsThe study sample consisted of 1674 patients, who had 4886 implants installed and were selected for statistical analysis. A total of 3219 implants were inserted in women and 1667 in men. The main outcome variable was early implant failure due to infection. There were 164 early failures in the study, accounting for 3.3% of the sample. Of those failures, 35 were a consequence of postoperative infection, resulting in 21.34% of early failures. The main risk factor identified was the presence of infection (odds ratio [OR] = 53.67, with 95% confidence interval [CI]).ConclusionsThe study's results suggest that infection may be considered a risk factor for early failure of osseointegrated implants.

Implant therapy is now an established treatment with high long-term success and survival rates. However, early implant failure, which occurs within one year of superstructure placement, occurs at a higher rate than late failure, which is represented by peri-implantitis caused by bacterial infection. Furthermore, various risk factors for early failure have been reported, including patient-related factors, such as systemic diseases, smoking, and bone quality and quantity, as well as surgery-related factors, such as surgeons' skill, osteogenesis technique, and selection of graft material, and implant-related factors, such as initial implant fixation and implant length diameter. Due to the wide variety of relevant factors reported, it is difficult to identify the cause of the problem. The purpose of this review is to discuss the risk factors associated with various types of bone augmentation which have a close causal relationship with early implant failure, and to determine the optimal bone grafting material for bone augmentation procedures to avoid early implant failure.

In Sub-Saharan Africa, obstetric fistulas are a health crisis of extensive proportions. Although risk factors for failure are described, little data exist regarding differences in risk factors for early and late recurrences. A retrospective cohort study was conducted to evaluate risk factors for fistula recurrence. Inclusion criteria included women who underwent repair of urogenital fistula at a Fistula Hospital in Uganda between 2013 and 2019. Our primary objective was to determine the incidence of both early and late failures and to identify and compare risk factors for each. Logistic regression was used to calculate crude odds ratios (ORs) and 95% confidence intervals (CIs) representing the association between each risk factor for early and late failures. Covariates significantly associated with early or late failure in univariate analyses were included in multivariate logistic regression models. A total of 541 patients were included. The incidence of early failure was 10.9%. Risk factors for early failure included stillbirth (aOR = 3.71, 95% CI: 1.38-9.96), fistula larger than 3cm, (aOR = 3.12 95% CI: 1.40-6.93), presence of foot drop (aOR = 4.74, 95% CI:1.88-11.97), and perioperative blood transfusion (aOR = 3.10, 95% CI: 1.11-8.66). Risk factors for late failures included stillbirth (aOR = 4.63, 95% CI:1.04-20.51), and previous fistula repairs (aOR = 3.13, 95% CI:1.30-7.56). Both early and late failures can occur and risk factors for each may be different. Identifying patients at risk for late failures is important for improved counseling and highlights the importance of developing risk-reducing strategies to improve patient outcomes after discharge.

Pneumatic dilation or myotomy for achalasia?

To identify risk factors for early failure of immediately placed implants in molar regions associated with three bone regenerative techniques. Ninety-two patients (44 women and 48 men; mean age 50 years, 35 smokers and 57 non-smokers) in need of a single implant crown to replace a molar were included. After placing the implant, patients were randomized to one of three treatment groups for bone reconstruction of remaining periimplant defects: Autologous bone (AB) chips, Ossix membrane (OM) or a combination of AB chips and OM. The implant was submerged, and after 4 months of healing a re-entry surgery was made to connect a healing abutment. Implants with a dehiscence on ≥2 sites (mesial/distal/oral/buccal) together with ≥50% visible threads, were judged as failures. A series of simple logistic regression analyses were performed to identify risk factors for failure among the following independent variables: sex, jaw, smoking status, plaque, bleeding on probing, fistula, extraction reason, mean initial periimplant defect size, treatment group, implant length, buccal bone dehiscence (BBD), soft-tissue dehiscence and infection. The identified risk factors entered a multiple logistic regression analysis. Fifteen implants failed before abutment operation (13 explantations/two non-osseointegrated). Treatment group had no impact on failure. Risk factors for failure were: Smoking >10 cigarettes/day (odds ratio [OR]=9.29, confidence interval [CI]=1.21-71.16), BBD (OR=11.43, CI=1.34-97.74) and infection (OR=36.7, CI=2.75-489.31). Implants placed immediately after extraction of a molar were associated with a high risk for failure at abutment operation. There was no difference in failure rate between three bone reconstructive techniques.

Risk Factors for Early Failure of Surgical Amputations: An Analysis of 8,878 Isolated Lower Extremity Amputation Procedures

The technique failure rate on peritoneal dialysis (PD) remains high despite technical progress. There are no data concerning the contribution of early failure to outcome on PD. To analyze the importance of early treatment failure in PD and to compare early with late failures with respect to reasons and predictors of risk for failure. We performed a retrospective study of all patients admitted for PD from October 1983 to June 2005. The end point was PD failure-free survival. Differences between reasons for failure with respect to early (within 6 months) and late failure were analyzed. Multivariate associations of baseline covariates with early and late failure were investigated. We included 279 patients. 153 (55%) patients experienced PD failure: 97 (63%) of them had technique failure; 56 (37%) patients died due to non-PD-related causes. 29% (n = 44) of all PD failures and 40% (n = 39) of all technique failures occurred within 6 months. Catheter and psychosocial problems contributed more often to early than to late failure, whereas infections, leakages, and hernias contributed equally to early and late failure. Death was the predominant reason for late failure. Female sex was a risk factor for early failure and older age a risk factor for late failure. Higher cholesterol levels were associated with a decreased risk for both early and late failure. The contribution of early failure to outcome on PD is important, as one third of all PD failures and 40% of all technique failures may occur within the first 6 months, as shown in our study. Due to the retrospective nature and the single-center character, the results cannot be generalized. However, it is important to enhance recognition of patients at high risk for early PD failure prior to initiation of PD, in order to avoid unnecessary surgical interventions and medical complications, and for rational resource allocation.

Performance and Failure of Right Ventricle to Pulmonary Artery Conduit in Congenital Heart Disease

In view of the above, the purpose of the present study was to review the literature on the main risk factors for early and late failure of implants. This study used the main databases: PUBMED and ScIELO to research scientific articles written between 1981 and 2018 in Portuguese and English. In the search we used the following descriptors: Postoperative Complications, Risk Factors, Treatment Failure; Dental Implants. Therefore, 65 articles were selected in which they fit the respective inclusion criteria. The studies showed that to minimize the occurrence of initial and final failures, it is mandatory to understand the pathogenesis and risk factors, to describe the signs and symptoms, and to clarify future clinical implications; where the majority of implant studies evaluate early and late failures separately. In the analysis of initial failures, some of the causal factors most frequently reported are smoking, infection, use of short implants, systemic factors, pre-existing periodontal disease and poor bone quality and quantity. It is concluded that there are innumerous risk factors for failure of implants, be it early or late; in the early faults, the main factors include smoking, either in the anterior or posterior region of the implant, and bone quality and quantity. While in the late failures we can mention the occlusal overload, parafunctional habits, the size of the implant, with respect to the diameter and length, as well as monitoring and maintenance of the prostheses.

Predictors for failure after Kasai operation

BackgroundStudies concerning total ankle arthroplasty could be influenced by several forms of bias. Independent national arthroplasty registries represent objective data on survival and patient reported outcomes. The aim of this study was to determine survival and identify risk factors for early failure in a nationwide series of total ankle arthroplasties from the Dutch Arthroplasty Register (LROI). Patients and methodsData of 810 patients, who received 836 total ankle arthroplasties between 2014 and 2020 were obtained from the Dutch Arthroplasty Register (LROI) with a median follow-up of 38 months (range 1–84 months). Survival was expressed in Kaplan-Meier analysis and associated hazard ratios for implant failure were determined. Implant failure was defined as the need for revision surgery for any reason or (pan)arthrodesis. ResultsDuring follow-up, we recorded 39 failures (4.7%) resulting in a implant survival of 95.3% with a median follow-up of 38 months (range 1–84 months). Medial malleolus osteotomy (HR = 2.27), previous surgery (HR = 1.83), previous osteotomy (HR = 2.82) and previous ligament reconstruction (HR = 2.83) all showed potentially clinically meaningful associations with a higher incidence of implant failure, yet only previous OCD treatment (HR = 6.21), BMI (HR = 1.09) and age (HR = 0.71) were statistically significant. InterpretationExcellent short-term survival (95.3%) with a median follow-up of 38 months was reported for TAA patients from the Dutch Arthroplasty Register. Patients with a lower age, a higher BMI or who had a prior surgical OCD treatment before TAA surgery appear to have a higher risk for revision after short-term clinical follow-up. Thorough patient selection with emphasis on risk factors associated with early implant failure might be essential to improve TAA survivorship.

This study aimed to identify risk factors associated with early implant failure in the anterior maxillary and mandibular regions. A total of 2023 patients with 2620 implants placed in the maxillary and mandibular anterior regions between January 2020 and June 2023 were included in this study. Clinical and radiographic data were extracted from medical records and imaging software. In organizing the information, 19 variables were categorized into patient-related factors (gender, age, periodontitis, reasons for tooth loss, bone quality, and penicillin allergy), implant-related factors (implant system, bone level/soft tissue level, diameter, and length), and surgical factors (jaw position, placement timing, bone grafting, bone compression/splitting surgery, concentrated growth factors (CGFs), bone graft materials, barrier membrane, torque, and healing style). Univariate and multivariate Cox proportional hazards regression models were used to identify significant risk factors for early failure. The cumulative survival rate (CSR) of all implants after a 0- to 43-month observation period was 95.6% (95% confidence interval [CI]: 94.8%-96.4%). Independent risk factors for early implant failure included non-submerged healing (hazard ratio [HR] = 3.000, 95% CI = 1.712-5.256), torque < 30 N/cm (HR = 13.193, 95% CI = 8.439-20.626), and Type I bone quality (HR = 3.220, 95% CI = 1.413-7.342) (all p < 0.05). Conversely, bone compression or splitting surgery was identified as a protective factor (HR = 0.344, 95% CI = 0.186-0.634). No significant associations were observed for age, reasons for tooth loss, penicillin allergy, use of CGF, or implant characteristics (location, type, length, and diameter). After 0-43 months of observation, the CSR for 2620 implants placed in 2023 patients was 95.6% (95% CI = 94.8%-96.4%). Torque < 30 N/cm, non-submerged healing, and Type I bone quality were considered independent risk factors for early implant failure in the anterior region.

Uneven data distribution (due to rare outcomes) and repeated measurements (from multiple implants per patient) hinder the creation of a precise oral implant failure risk model. The aim of this study was to explore variable selection methods suitable for oral implant data, assess risk factors of early failure and postoperative complications, and apply the two-step cluster analysis to establish a risk prediction model for oral implant failure, providing a reference for clinical practice. This study was a retrospective analysis, with early failure and postoperative complications serving as the outcome indicators. Given the repeated measurements and uneven distribution in oral implant data, our study conducted a comparative analysis between GEE and GEE with Firth penalization. This study evaluated the influencing factors screened by a more suitable model and utilized them for subsequent risk prediction. A two-step cluster analysis was applied to identify different subgroups of early failure and postoperative complications; their clinical characteristics were compared, and relevant risk prediction models were developed. Among a total of 677 patients and 1200 implants, 21 implants were lost prior to loading, and postoperative complications occurred in 74 patients involving 94 implants. The GEE model with Firth's penalty term indicated that non-submerged healing (p < 0.001), shorter implant length (p < 0.001), and thinner diameter (p = 0.007) were risk factors for early failure. The GEE model showed that non-submerged healing (p = 0.039) was a protective factor against postoperative complications, whereas unhealed extraction sockets at the implant site (p = 0.048), the use of bone substitutes (p = 0.008), and a history of periodontal disease (p = 0.009) were risk factors. Additionally, the use of bovine tendon-derived absorbable biomembranes (p = 0.036) may elevate the risk of postoperative complications. The two-step cluster analysis identified two patient subgroups, categorized as high-risk and low-risk, and the prediction model demonstrated good discrimination ability. Early failure data were highly imbalanced, and the incorporation of the Firth penalty term provided significant benefits. However, its effectiveness in managing postoperative complication data remained limited. Thus, a one-size-fits-all approach to variable screening may not have suited all types of imbalanced data. The analysis conducted in this study, using specific screening techniques, yielded more reliable influencing factors. Additionally, the developed two-step clustering model was capable of predicting high-risk patients for early failures and postoperative complications before surgery, aiding clinicians in devising personalized preventive measures to reduce incidence rates. Clinical trial registration number: ChiCTR2300070420.