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« A Retrospective Analysis of Multiple Dental Implant Failures »
by
« Elahe Behrooz, DDS, MBA, MSc Candidate »
A thesis submitted in conformity with the requirements for the degree of « Masters in Prosthodontics »
« Graduate Prosthodontics Department » University of Toronto
© Copyright by « Elahe Behrooz » « 2019 »
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« A Retrospective Analysis of Multiple Dental Implant Failures »
« Elahe Behrooz »
« Masters in Prosthodontics »
« Graduate prosthodontics Department »
University of Toronto
« 2019 »
Abstract
Purpose: To identify and compare possible risk indicators associated with failure of
multiple versus single dental implants
Materials and Methods: A retrospective analysis was performed on patients with more
than one implant who had experienced biological failure of one or more implants at the
Faculty of Dentistry, University of Toronto (January 1979 to June 2018). Data was used
to identify possible factors associated with multiple dental implant failures and compare
these factors between individuals with single and multiple implant failures. Associations
between various factors and multiple implant failure were reported.
Results: Excluding history of implant failure, the following factors were found to be
associated with MIF: machined surfaces, post-operative infections, presence of certain
prostheses opposing the implant, periodontitis, alcohol consumption, history of
chemotherapy, and use of antidepressants.
Conclusions: Provision of implant-based care for patients presenting with factors
associated with multiple implant failure should be undertaken with caution.
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Acknowledgments
I would like to gratefully appreciate the efforts of the individuals who greatly contributed to this
project: my thesis supervisors Drs. David Chvartszaid and Amir Azarpazhooh and my committee
member Dr. Jim Yuan Lai.
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Table of Contents
Acknowledgments ......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables .................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
List of Appendices ....................................................................................................................... viii
List of Abbreviations in Alphabetical Order …………………………………………………….ix
1 Review of the Literature ............................................................................................................... 1
1.1 Dental Implants ............................................................................................................... 1
1.2 Osseointegration .............................................................................................................. 2
1.3 Radiographic and Histologic Appearance of Osseointegration ....................................... 3
1.4 Osseous Healing, Osteoinduction, Osteoconduction and Bone Remodeling .................. 4
1.5 Definitions of Implant Success ........................................................................................ 5
1.6 Pathophysiology and Diagnosis of Implant Failure ...................................................... 12
1.7 Etiology of Implant Failures .......................................................................................... 14
1.8 Risk Indicators Associated with Implant Failures ......................................................... 17
1.8.1 Smoking ..................................................................................................................... 18
1.8.2 Diabetes ..................................................................................................................... 19
1.8.3 History of Periodontitis ............................................................................................. 20
1.8.4 History of Implant Failure ......................................................................................... 20
1.8.5 Occlusal Forces and Bruxism .................................................................................... 21
1.8.6 Surgical Experience ................................................................................................... 22
1.8.7 Medications ............................................................................................................... 22
1.8.8 Other Risk Factors ..................................................................................................... 23
1.8.9 Large-Scale Studies Examining Risk Indicators for Implant Failure ........................... 24
1.9 Management of Implant Failures ........................................................................................ 33
1.10 Timely Identification of Failed Implants ........................................................................... 33
1.11 Multiple Implant Failures (Cluster Phenomenon) ............................................................. 34
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1.12 Implications of Research ................................................................................................... 43
2 Purpose and Statement of Problem ............................................................................................. 44
2.1 Purpose .......................................................................................................................... 44
2.2 Statement of the Problem .............................................................................................. 44
3 Aims, Objectives and Hypothesis ............................................................................................... 45
3.1 Aims and Objectives ...................................................................................................... 45
3.1.1 Primary Objective: ......................................................................................................... 45
3.1.2 Secondary Objectives: ................................................................................................... 45
3.2 Hypotheses .................................................................................................................... 45
4 Manuscript for Future Publication .............................................................................................. 46
5 Discussion ................................................................................................................................... 75
5.1 Comparison of Results to Existing Literature ..................................................................... 75
5.1.1 Multiple Implant Failure Studies .................................................................................. 75
5.1.2 Implant Failure Studies ................................................................................................. 77
5.2 Limitations of the Study ...................................................................................................... 83
5.3 Strengths of the Study ......................................................................................................... 85
6 Recommendations for Future Research and Clinical Practice ................................................... 87
7 Summary and Conclusions ......................................................................................................... 87
8 Bibliography ............................................................................................................................... 88
Appendices .................................................................................................................................... 99
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List of Tables
Table 1: Systematic reviews on success and survival of implants and implant-supported
prostheses in restoring complete and partial edentulism
Table 2: Studies indicating magnitude of effect and biological plausibility of risk
indicators (smoking, diabetes, history of periodontitis, and history of implant failure) on
implant failure
Table 3: Summary of cohort studies on multiple implant failure
Table 4: Newcastle-Ottawa Scale assessment of the cohort studies on multiple implant
failure
Table 5: Factors associated with implant failure and failure of multiple implants
Table 6: Patient-level risk indicator analysis using a univariate estimating equations
logistic regression model
Table 7: Implant-level risk indicator analysis using a univariate estimating equations
logistic regression model
Table 8: Model A generated by inclusion of all factors which presented with significance
at the univariate level (including history of implant failure)
Table 9: Model B generated by inclusion of all factors which presented with significance
at the univariate level (excluding history of implant failure)
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List of Figures
Diagram 1: Schematic representation of dental implant failure etiology
Diagram 2: Schematic representation of biological risk indicators for failure of dental
implants
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List of Appendices
Appendix A: Data extraction form
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List of Abbreviations in Alphabetical Order
-CI: confidence interval
-CRDP: complete removable dental prosthesis
-ed: edentulous
-FA ISFDP: full-arch implant-supported fixed dental prosthesis
-FPDP: fixed partial dental prosthesis
-FU: follow-up
-ISFCDP: implant-supported fixed complete dental prosthesis
-ISFDP: implant-supported fixed dental prosthesis
-ISFPDP: implant-supported fixed partial dental prosthesis
-ISOD: implant-supported overdenture
-ISP: implant-supported prosthesis
-ISRDP: implant-supported removable dental prosthesis
-ISSC: implant-supported single crown
-Md: mandible
-Med: median
-MIF: multiple implant failure
-Mx: maxilla
-NOS: Newcastle Ottawa Scale
-NSAIDs: non-steroidal anti-inflammatory drugs
-OD: overdenture
-OR: odds ratio
-pt: patient, pts: patients
-Ref: reference category
-RPDP: removable partial dental prosthesis
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-RR: risk ratio
-SD: standard deviation
-SIF: single implant failure
-sig: significant
-SSRIs: selective serotonin reuptake inhibitors
-st: study
-tx: treatment
-y: year[s]
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This thesis comprises of the following components:
Chapter 1) review of the literature
Chapter 2) purpose and statement of the problem
Chapter 3) aims, objectives and hypotheses
Chapter 4) manuscript for future publication
Chapter 5) discussion
Chapter 6) recommendations for future research and clinical practice
Chapter 7) summary and conclusions
Chapter 8) bibliography
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1 Review of the Literature
1.1 Dental Implants
Titanium dental implants have been utilized to support intraoral and extraoral prostheses
since the late 1960s with high survival and success rates (Branemark et al. 1977;
Branemark 1983). The initial studies on osseointegrated dental implants were performed
by Branemark at the University of Gothenburg, Sweden, and the success of the implants
was attributed to the use of titanium screws inserted in bone via minimally traumatic
surgical techniques and a period of undisturbed healing (Albrektsson et al. 1988). Later, a
15-year follow-up of the Swedish patients (Adell et al. 1981) and the Toronto conference
in 1982 led to acceptance of the osseointegration concept and expansion of its use from
completely edentulous jaws to restoration of partial edentulism and single missing teeth.
Numerous subsequent prospective multicenter studies assessed, defended and broadened
the clinical applicability of dental implants (Adell et al. 1981; Albrektsson 1988).
As average life expectancy is increasing, the number of older patients with partial
edentulism is expected to increase (Felton 2016). Despite improvements in oral health
care leading to a lower proportion of cases of complete edentulism in the population, with
more patients over 65 years of age, the absolute number of patients with complete
edentulism is expected to remain constant (Felton 2016). The global market utilization of
dental implants is steadily growing at a compound annual growth rate of 6.1% and is
expected to reach USD 4.4 billion by 2022. This growth is attributable to a rise in oral
health awareness, increased demand for preventive and cosmetic dental procedures, and
an aging population presenting with complete and partial edentulism (Meticulous
Research 2017).
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1.2 Osseointegration
The concept of tissue-integrated prostheses was developed in 1952 at the Laboratory of
Vital Microscopy at the University of Lund and later expanded at the Laboratory for
Experimental Biology at the University of Goteborg (Branemark 1983). In the early
1960s, animal experiments into microcirculation lead to the incidental discovery that
optical chambers could not be removed from adjacent bone after adequate healing, since
the bone tissue had visibly grown into the micro-irregularities on the titanium surface. In
other words, a shell of compact bone without any soft tissue intervention had formed
between the bone and the implant surface. Success of osseointegration in animal studies
led to treatment of the first patient with osseointegrated titanium implants in 1965
(Branemark 1983). Currently, osseointegration is defined as a direct functional and
structural connection between living bone and the surface of a load-bearing implant
(Albrektsson et al. 1981). This concept has contributed to high predictability in
prosthodontic rehabilitation of completely and partially edentulous patients (Albrektsson
et al. 1981; Branemark 1983). Zarb and Albrektsson (1991) described osseointegration
further as a “process in which a clinically asymptomatic rigid fixation of alloplastic
material is achieved and maintained in bone during functional loading”. Radiographic
assessment of an osseointegrated implant demonstrates a seemingly direct contact
between the bone and the implant. Histologically, anchoring bone closely follows the
micro-irregularities of the implant surface: light microscopy shows anatomic congruence
of the anchoring bone onto the titanium surface geometry, and scanning electron
microscopy shows bone cells – particularly, osteoblasts – adapted to the titanium surface
(Branemark 1983; Albrektsson and Jacobsson 1987; Palmquist et al. 2012).
Esposito et al. (1998) provided several other definitions of osseointegration from
different perspectives:
• From the patient’s viewpoint, an implant is osseointegrated if it provides a stable and apparently immobile support of prostheses under functional loads, without pain, inflammation or loosening over the lifetime of the patient.
• From a viewpoint of macro- and microscopic biology and medicine, osseointegration of a fixture in bone is defined as the close apposition of new and reformed bone in congruence with the fixture, including surface irregularities, so that at light microscopic level there is no interpositioned connective or fibrous tissue and that a direct structural and functional connection is established, capable
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of carrying normal physiological loads without excessive deformation and without initiating rejecting mechanisms.
• From a macroscopic biomechanical point of view, a fixture is osseointegrated if there is no progressive relative motion between the fixture and surrounding living bone and marrow under functional levels and types of loading for the entire life of the patient and exhibits deformations of the same order of magnitude as when the same loads are applied directly to the bone.
• From a microscopic biophysical point of view, osseointegration implies that at light microscopic and electron microscopic levels, the identifiable components of tissue within a thin zone of a fixture surface are identified as normal bone and marrow constituents which continuously grade into a normal bone structure surrounding the fixture, that mineralized tissue is found to be in contact with the fixture surface over most of the surface within nanometers so that no functionally significant intervening material exists at the interface (Esposito et al. 1998(I)).
1.3 Radiographic and Histologic Appearance of Osseointegration
Osseointegration is seen radiographically as a seemingly direct contact between the
implant and the surrounding bone. The histologic appearance of osseointegration under
light microscopy is dense lamellar bone with well-organized concentric lamellae
following the micro-irregularities of the implant surface. Scanning Electron Microscopy
evaluation reveals an amorphous layer (20-40 to 500 nm thick) of collagen and calcified
tissue. The collagen filaments reach as close as about 20 nm from the implant surface,
and the last 20 to 30 nm of the interface towards the metal is covered by partly calcified
amorphous ground substance (proteoglycans and glycosaminoglycans). Cell processes
from connective tissue and osteogenic cells on titanium surface can be visualized. The
mechanism of anchorage of collagen filaments resembles Sharpey’s fibers, and similarly
they provide a biological seal (Albrektsson and Jacobsson 1987; Palmquist et al. 2012).
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1.4 Osseous Healing, Osteoinduction, Osteoconduction and Bone
Remodeling
Bone is a mineralized, dynamic, vascular connective tissue. It is arranged in two macro-
architectural forms: trabecular (cancellous, spongy) and cortical (compact). The main
cells in bone tissue are osteoblasts which synthesize new bone matrix, osteoclasts which
are the main bone resorbing cells, osteocytes which are pivotal cells in regulation of bone
mass and supporting the bone structure, and undifferentiated mesenchymal cells which
eventually transform into osteo-progenitor cells (pre-osteoblasts) and differentiated bone
cells. Bone undergoes constant remodeling and is capable of scar-free healing and
regeneration (Davies 2003; Insua et al. 2017).
After any trauma to the bone, an inflammatory process occurs during which a mediator
cascade promotes hematoma formation and circulatory alteration. Regeneration and bone
fill in the wound follow, and the bone later remodels and matures. After placement of
dental implants, a similar process occurs with a series of immune-inflammatory
responses, angiogenesis and osteogenesis. Following adequate regeneration, direct
contact occurs between the surface of the implant and bone in about 8-12 weeks.
Lamellar bone formation initiates the biological stability or osseointegration. Therefore,
the mechanism of osseointegration is similar to the mechanism of primary bone healing
(Santos et al. 2002; Insua et al. 2017).
In general, bone growth and remodeling occur through osteoinduction and
osteoconduction processes (Insua et al. 2017). Osteoinduction is defined as the
recruitment of immature undifferentiated cells and their transformation into osteoblasts
that promote osteogenesis. This biological process starts immediately after injury and is
very active during the first week thereafter (Davies 2003; Insua et al. 2017).
Osteoconduction is the growth of bone on a surface or scaffold. This phenomenon is
possible on titanium and contributes to contact osteogenesis (see below), which is one of
the main bone formation mechanisms around osseointegrated dental implants (Davies
2003; Insua et al. 2017).
Two main processes have been introduced to explain bone formation around dental
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implants: Distance Osteogenesis and Contact Osteogenesis (Davies 2003). Distance
Osteogenesis is described as osteogenic cells developing on old bone surfaces,
transforming into polarized osteoblasts and producing newly formed peri-implant bone
matrix and trabeculae from the host bone cavity towards the implant surface. This process
occurs in cortical bone healing and results in bone approaching implant surface but being
separated from the implant by osteoblasts. These cells will be trapped and eventually die
in the space between the external surface of the implant and the newly formed bone
(Davies 2003). Contact Osteogenesis encompasses the processes of osteoconduction and
de novo bone formation. During Contact Osteogenesis, osteogenic cells form on the
implant surface and osteoblasts secrete new bone matrix on the implant surface, resulting
in newly formed peri-implant bone developing from the implant towards the healing
bone; hence, this process is more favorable than distance osteogenesis due to absence of
dead osteoblasts in direct contact with the implant surface (Davies 2003; Santos et al.
2002).
1.5 Definitions of Implant Success
The primary purpose of an osseointegrated implant is to provide support and retention to
a fixed or removable dental prosthesis. In assessing an implant’s ability to fulfill its
intended objective, three clinical outcomes can be distinguished – implant success,
implant survival and implant failure.
The most reliable parameters for clinically-relevant assessment of osseointegration are
radiographic examination and implant mobility (Esposito et al. 1998(I)). An implant is
said to be successful when it fulfills a set of predefined success criteria. Smith and Zarb
(1989) proposed six criteria for success of dental implants: (1) clinical immobility of an
individual unattached implant, (2) absence of radiographic evidence of peri-implant
radiolucency, (3) vertical bone loss less than 0.2 mm annually following the implant’s
first year of service, (4) absence of persistent and/or irreversible signs and symptoms
(e.g., pain, infections, neuropathies, paraesthesia, or violation of the mandibular canal),
(5) implant design allowing placement of a prosthesis with satisfactory appearance, and
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(6) a success rate of 85% at the end of a 5-year observation period and 80% at the end of
a 10-year period. The first five of these criteria can be used to assess clinical care
outcome of an individual implant. An implant that does not fulfill its intended functions
of retention and support is said to have failed. Implant survival is observed when an
implant is present in the mouth and serves its intended function but does not fulfill all the
predefined success criteria. Several other authors have proposed similar success criteria.
For example, Buser et al. (1997) defined implant success as: (1) absence of persistent
subjective complaints (e.g., pain, foreign body sensation, and/or dysesthesia), (2) absence
of recurrent peri-implant infection with suppuration, (3) absence of mobility, and (4)
absence of a continuous radiolucency around the implant.
Implant treatment outcomes can be assessed by the presence of problems and
complications at different levels:
• at the implant level: mobility, pain, radiolucency, infection and peri-implant bone
loss (<1.5 mm in 1st year, <0.2 mm annually thereafter).
• at the peri-implant soft-tissue level: suppuration, bleeding, and swelling.
• at the prosthetic level: technical complications, prosthetic maintenance, and
problems with function.
• at the patient level: symptoms (discomfort and paresthesia), dissatisfaction with
appearance and function (Papaspyridakos et al. 2012).
Implant success needs to be differentiated from treatment success. Treatment can be
successful despite the failure of one of the implants if the remaining implants are able to
continue supporting a prosthesis. Conversely, treatment can be unsuccessful because of
patient dissatisfaction with aesthetics despite the success of the individual supporting
implants.
Three additional related concepts need to be distinguished – implant failure, implant
removal and implant loss. Implants that are no longer present in the mouth for any reason
are said to have been lost. Implant loss and implant removal are not synonymous because
some implants may have exfoliated on their own without them needing to be physically
removed. Implant loss can occur for several reasons including (biologic) implant failure
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and, hence, implant loss is always equal to or greater than implant failure. Other reasons
for implant removal include implant malposition, implant fracture, and iatrogenic damage
(e.g., paresthesia). Unfortunately, studies are not consistent in the use of the terminology,
and implant failure is sometimes used synonymously with implant loss. When this
occurs, the rate of implant failure is overestimated (Antalainen et al. 2013).
Concepts of implant success and survival are related but distinct entities. Analysis of
survival is simpler and less stringent than analysis of success: survival is assessed by
counting the number of osseointegrated implants that are present in the oral cavity. By
contrast, analysis of success involves assessment of several predefined criteria associated
with the health and quality of the implants and the associated prostheses. This difference
in assessment frequently translates into higher survival rates than success rates: in other
words, it is possible for an implant to be “surviving” (i.e., present in the mouth and
osseointegrated) but not to be “successful” (i.e., not fulfilling at least one of the
predefined success criteria) (Moraschini et al. 2015).
Longitudinal effectiveness of dental implants has been well demonstrated. Original
research from machined surface implants treated via a two-stage protocol has now
expanded to textured surface implants with new healing and loading protocols for
optimized restoration of function and esthetics. Three systematic reviews have reported
clinical outcomes with osseointegrated implants in completely edentulous patients (see
Table 1) (Papaspyridakos et al. 2014; Moraschini et al. 2015; Kern et al. 2016).
Systematic review by Papaspyridakos et al. (2014) included 17 prospective studies, 501
patients and 2,827 implants. This study aimed to report on the implant and prosthodontic
survival rates associated with implant-supported full-arch fixed prostheses for edentulous
mandibles after an observation period of a minimum of 5 years. The authors only
included studies with solid screw-type implants, excluding zygomatic, pterygomaxillary
and transitional implants. This study reported cumulative 5- and 10-year implant survival
rates of 98.4% and 96.8% for moderately rough surface implants. The corresponding
implant survival rates for machined surface implants over the same time periods of 5 and
10 years were 98.9% and 97.8% respectively. In general, implant and prosthesis survival
rates for treatment of the edentulous mandible exceeded 96% (Papaspyridakos et al.
2014).
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Similarly, Kern et al. (2016) conducted a systematic review on 54 studies, 2,368 patients,
9,726 implants, 899 implant-supported fixed dental prostheses and 1,442 implant-
supported overdentures. This study analyzed implant and prosthesis survival rates post-
prosthetic loading after an observation period of at least 3 years to assess the potential
impact of implant location (maxilla vs. mandible), implant number per patient, prosthesis
type (removable vs. fixed) and the type of attachment system (screw-retained, ball vs. bar
vs. telescopic crowns) on the implant survival rates. The authors included edentulous
patients with machined or moderately rough surface endosseous implants irrespective of
their number, length, diameter, position or angulation, and which were placed into either
native or augmented bone. The review included studies with fixed and removable
prosthodontic rehabilitations undergoing immediate, early or delayed loading. The
authors reported a 5-year implant survival rate of 97.9% in the edentulous maxilla and
98.9% in the edentulous mandible (Kern et al. 2016).
Moraschini et al. (2015) included 23 studies, 7,711 implants and 2,211 patients. This
study aimed at evaluating the survival and success rates of implants in longitudinal
studies with follow-up periods of at least 10 years. It included both partially and
completely edentulous patients. The study reported a cumulative survival rate of 94.6%
(SD 5.97%) during a mean follow-up time of 13.4 years for implants in partially and
completely edentulous patients.
Overall, these three systematic reviews (Papaspyridakos et al. 2014; Moraschini et al.
2015; Kern et al. 2016) reported similar and high success and survival rates for dental
implants in the completely edentulous patients.
Three systematic reviews on implant success and survival in partially edentulous jaws
demonstrated similar findings, suggestive of very high implant survival rates (see Table
1) (Lindh et al. 1998; Creugers et al. 2000; Jung et al. 2008). Lindh et al. (1998)
conducted a systematic review to assess the survival of implants supporting fixed partial
dental prostheses and single crowns in partially edentulous patients. The authors included
studies on threaded cylindrical implants with a minimum follow-up period of 1 year of
loading and reported on 2,686 implants (570 supporting single crowns and 2,116
supporting fixed partial dental prostheses) during a follow-up of 1-8 years. Implant
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survival rates of 97.5% and 93.6% as well as success rates of 97.2% and 85.7% were
reported for implant-supported single crowns and fixed partial dental prostheses
respectively (Lindh et al. 1998).
Creugers et al. (2000) systematically reviewed clinical studies on the performance of
implant-supported single-tooth restorations with a minimum follow-up of 2 years. The
authors reviewed 9 studies on 459 implants and reported implant survival rates of 97+/-
1% (Creugers et al. 2000). Systematic review by Jung et al. (2008) assessed the 5-year
survival of implant-supported single crowns and described the incidence of biological and
technical complications. The authors reviewed 26 studies on 1,558 implants and reported
an annual failure rate of 0.28 for the implant-supported prostheses with a 96.8% survival
rate (Jung et al. 2008).
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Table 1: Systematic reviews on success and survival of implants and implant-supported prostheses in restoring complete and partial edentulism
Type of edentulism
Study (author,
year)
Number of
included studies
Number of included patients / implants / prostheses
Follow-up period, success rate / survival
rate
Types of prostheses
Comments / additional findings
complete edentulism
Papaspyridakos et al. (2014)
17 501 pts
2,827 implants
FU: at least 5y
cumulative implant survival rates:
-moderately rough surface implants: 98.42% (95% CI: 97.98-98.86%) (5y) to 96.86% (95% CI: 96-97.73%) (10y)
-machined surface implants: 98.93% (95% CI: 98.38-99.49%) (5y) to 97.88% (95% CI: 96.78-98.98%) (10y)
single-piece Md ISFCDP
-implant surface had no effect on implant survival in ed Md -number of supporting implants and implant distribution had no effect on implant survival
complete edentulism
Kern et al. (2016)
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2,368 pts 9,267 implants 899 ISFDPs
1,442 ISODs
FU: 3y and 5y 5y implant survival rate: -Mx: 97.9% (95% CI: 97.4-98.4%) -Md: 98.9% (95% CI: 98.7-99.1%)
-Mx: sig higher implant loss rate with removable prostheses than fixed 0.28 (95% CI: 0.21-0.38) vs. 2.31 (95% CI: 1.56-3.42, p < 0.0001)
ISFCDP -risk of implant loss more than three times higher with <4 implants: 7.22 (95% CI: 5.41-9.64) vs. 2.31 (95% CI: 1.56-3.42, p<0.0001)
complete and partial edentulism
Moraschini et al. (2015)
23 2,211 pts 7,711 implants
FU: 13.4y -cumulative implant survival rate: 94.6% (SD 5.97%)
FPDP (3 st), ISSC/FPDP (5 st), ISOD (4 st), ISFCDP (4 st), ISSC (3 st), ISSC/FPDP/ISFCDP (4 st)
-cumulative implant survival rates: 10y: 96.5% (SD: 3.15) 12y: 95.4% (SD: 4.31) 14-15y: 94.9% (SD: 2.89) 16y: 88.8% (SD: 7.21) 20y: 91.2% (SD: 12)
-cumulative mean survival rate: 94.6% (SD: 5.97)
partial Lindh et 19 2,686
FU: 1-8y ISSC and -no CI reported
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edentulism al. (1998) (9 on
single implants, 9 on FPDP, 1 on both)
implants (570 supporting ISSCs, 2,116 supporting FPDPs)
-ISSC: survival rate: 97.5% success rate: 97.2% -IS-FPDP: survival rare: 93.6% success rate: 85.7%
-cumulative implant survival rate: >90%
ISFPDP
partial edentulism
Creugers et al. (2000)
9 459 implants
FU: minimum 4y - implant survival rate: 97+/- 1%
ISSC
partial edentulism
Jung et al. (2008)
26 1,558 implants
FU: minimum 5y -implant survival rate: 96.8% (95% CI: 95.9-97.6%)
-annual failure rate: 0.28 of implant-supported prostheses (95% CI: 0.14-0.59)
ISSC - 54 implants lost: 30 (1.9%) prior to functional loading and 24 in function
Abbreviations: CI (confidence interval), ed (edentulous), FPDP (fixed partial dental prosthesis), FU (follow-up), ISFCDP (implant-supported fixed complete dental prosthesis), ISFDP (implant-supported fixed dental prosthesis), ISP (implant-supported prosthesis), ISSC (implant-supported single crown), Md (mandible), Mx (maxilla), OD (overdenture), pts (patients), SD (standard deviation), sig (significant), st (study), tx (treatment), y (year[s]).
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1.6 Pathophysiology and Diagnosis of Implant Failure
Loss of osseointegration clinically manifests as a peri-implant radiolucency and implant
mobility. It may present with additional clinical signs of infection, pain or sensitivity,
enlarged soft tissues, suppuration, swelling, fistulation, color changes of the marginal
peri-implant tissues, etc. These additional clinical signs are shared with certain other
biologic complications such as peri-implant infection; therefore, unlike peri-implant
radiolucency and implant mobility, they are not pathognomonic for implant failure
(Esposito et al. 1998(I)).
Most common diagnostic criteria employed for the diagnosis of implant failures are the
following:
Clinically discernible mobility: A clinical distinction has to be made between the
mobility of a poorly connected abutment or prosthesis and the mobility of the underlying
implant. If the implant itself is deemed to be mobile, a fibrous tissue capsule surrounds it.
Mobility of an implant following an adequate healing period is a cardinal sign of implant
failure. Different types of implant mobility may be detected such as rotation, lateral or
horizontal, axial or vertical mobility. The necessity of removing fixed multi-unit implant-
supported prostheses, which is demanding and time-consuming, has made intraoral
conventional radiography a valuable aid in determining the success of oral implants in
clinical practice. Initial rotational mobility, in the absence of vertical and horizontal
mobility, may indicate a weak or immature bone-to-implant interface and may not be
associated with the presence of a soft tissue capsule. However, horizontal and vertical
mobility invariably reflects bone loss along the entire surface of the implant and the
presence of a peri-implant soft tissue capsule (Esposito et al. 1998(I); Papaspyridakos et
al. 2012).
Pain or sensitivity may be associated with implant mobility and could be one of the first
signs indicating an implant failure. However, this test lacks both specificity and
sensitivity as failed implants can also be completely asymptomatic, while symptoms of
pain may be associated with other implant-related biologic complications such as an
infection as well as with post-operative intraosseous edema and pressure on the inferior
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alveolar nerve (Esposito et al. 1998(I); Papaspyridakos et al. 2012).
Dull sound on percussion of a properly seated abutment or single-unit prosthesis is
pathognomonic for implant failure. This test is conducted by gently percussing the
abutment or single-unit prosthesis with a loosely held metallic instrument. A subdued
sound upon percussion indicates soft tissue encapsulation, whereas a clear crystalline
sound indicates successful osseointegration of the implant. Although the percussion test
is a subjective test, it does provide useful indication to the examiner (Esposito et al.
1998(I)).
Clinical signs of infection such as swelling, fistula, suppuration, and tenderness may be
observed in association with a failed implant – either as the cause of implant failure or as
a consequence of secondary bacterial colonization of the space between the soft tissue
capsule and the surface of the implant. However, clinical signs of infection can also arise
around osseointegrated implants and, at the same time, implant failure may present in the
absence of clinical signs of infection (Esposito et al. 1998(I); Papaspyridakos et al. 2012).
Radiographic assessment: It is crucial to establish a radiographic baseline for
assessment of future marginal bone changes and to perform regular radiographic re-
assessments via standardized peri-apical radiographs to detect any changes associated
with the bone surrounding the implant. Two distinct radiographic presentations have been
associated with failure of dental implants: thin peri-implant radiolucency surrounding the
entire implant and severe marginal bone loss extending to the apex of the implant
(Esposito et al. 1998(I, II)).
Fibrous encapsulation is the soft tissue surrounding the failed dental implant instead of
an intimate bone-to-implant contact. Histologically this phenomenon presents as a loose
connective tissue layer between healthy bone and the implant surface. It is also called
‘locus minoris resistantiae’, or the path of least resistance, allowing small relative
movements between the implant and bone, increasing the risk of inflammatory reactions
and propagation of bacteria. Implants surrounded by fibrous encapsulation may present
with clinical mobility and subdued sound upon percussion (Esposito et al. 1998(I, II)).
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1.7 Etiology of Implant Failures
Branemark et al. (1977) and several other authors have attempted to outline absolute and
relative contraindications to dental implant treatment. Improved contemporary
understanding of implant failure and the associated risk factors has resulted in a
significant shortening of the list of absolute contraindications for implant surgery
originally proposed by Branemark et al. (1977), thereby expanding the scope of implant
therapy. Currently, only patients with a history of high dose radiation therapy to the head
and neck region, high dose intravenous bisphosphonate therapy, unstable psychiatric
status, and inability to maintain oral care for implants are considered not to be candidates
for implant treatment (Clementini et al. 2014). However, it is important to highlight that
while the literature provides some guidance on risk factors for implant failure at the
population level, in clinical practice it is difficult to identify patients who are most likely
to experience failure of dental implants, and it is similarly difficult to explain the etiology
of implant failure in any given patient once it occurs (Duyck and Naert 1998).
The etiology of implant failure can be divided into biological implant failure (i.e., the
failure of the osseointegration phenomenon) and other types of failure (mechanical,
iatrogenic, and patient adaptation) (Esposito et al. 1998(I, II)). Biological Failure of
endosseous implants is defined as an inadequacy of the host tissue to establish or to
maintain osseointegration. From a temporal standpoint, biological failures can be divided
into early and late failures. Early implant failure occurs in the first few months after the
surgical intervention to insert the implant and before prosthodontic restoration and is
considered to represent a failure to establish osseointegration. Late implant failure occurs
after the initial healing period of a few months and after prosthetic rehabilitation and is
considered to represent failure to maintain established osseointegration (see Diagram 1).
Aside from biological implant failure, other types of dental implant failure include
mechanical failure (e.g., implant fracture), iatrogenic failure (e.g., nerve damage as a
result of a surgical intervention to place an implant, non-restorable implant due to
malposition), and inadequate patient adaptation (e.g., phonetic, esthetic and psychological
problems) (Esposito et al. 1998(I, II)).
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Diagram 1: Schematic representation of dental implant failure etiology
Early implant failures are hypothesized to occur due to anatomical conditions and
surgical trauma, whereas bone quantity and quality, overload and peri-implantitis are
hypothesized to be the major contributors to late implant failures. It is not well
understood whether bacterial infection and overload play significant roles in failure of
dental implants; however, the nature of the implant surface is known to be an important
factor (Esposito et al. 1998(I, II)).
In general, factors related to biological failures can be divided into three categories:
1. health- and social history-related factors: medical status of the patient,
smoking, irradiation therapy, parafunction, presence of periodontitis, quantity and
quality of bone, etc. (Bain and Moy 1993; Esposito et al. 1998(I, II); Jemt and
Hager 2006; Moraschini et al. 2016; Sousa et al. 2016).
2. operator- and surgery-related factors: bone grafting, bacterial contamination
during surgery, operator experience, degree of surgical trauma, number of
implants being placed, implant lengths, etc. (Smith et al. 1992; Esposito et al.
1998(I, II); Jemt and Hager 2006; Clementini et al. 2014; Sendyk et al. 2017).
etiology of implant failure
biological
early (during the first few months after surgery)
late (after prosthetic
rehabilitation)
mechanical iatrogenic patient adaptation
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3. post-surgical factors: bacterial contamination, immediate loading, number of
implants supporting a prosthesis and prosthetic factors, etc. (Esposito et al.
1998(I, II); Carr et al. 2019).
Osseointegration is an outcome of a wound-healing phenomenon, and its maintenance is
intimately tied to bone homeostasis. Factors that interfere with biologic processes of
wound healing and bone homeostasis can prevent osseointegration from arising or result
in its breakdown. Several possible risk factors for biological implant failure have been
identified (Smith et al. 1992; Duyck and Naert 1998; Jemt and Hager 2006; Moraschini et
al. 2015; Chrcanovic et al. 2017), and these are generally divided into two groups:
endogenous factors and exogenous factors (see Diagram 2). Endogenous factors relate to
the patient and are further divided into systemic factors (e.g., diabetes, smoking) and
local factors (e.g., parafunction, presence of periodontitis, quantity and quality of bone).
Exogenous factors are independent of the patient and are divided into operator-related
factors (e.g., operator experience, surgical technique) and biomaterial-related factors
(e.g., implant design) (Esposito et al. 1998(I, II)).
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Diagram 2: Schematic representation of biological risk factors for failure of dental
implants
1.8 Risk Indicators Associated with Implant Failures
Risk indicators in implant treatment are the local and general elements that increase the
risk of failure of dental implants through various biologic mechanisms. Early literature on
osseointegration emphasized micro-topography of the implant surface, atraumatic
handling of the recipient bone site, absence of contamination, quality and quantity of
bone, and adequate period of undisturbed healing as critical determinants of
osseointegration success (Branemark 1983). Other risk indicators have also been
introduced in the literature such as location of the implant (posterior maxilla), bruxism,
risk factors for biological implant
failure
endogenous factors
systemic factors (diabetes, smoking,
etc.)
local factors (parafunction,
periodontitis, quantity and quality of bone, etc.)
exogenous factors
operator-related factors
operator experience
surgical technique
biomaterial-related factors (implant design,
etc.)
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shorter implants, irradiation to the head and neck, lack of surgeon’s experience,
compromised initial stability as well as immediate placement and loading (Chrcanovic et
al. 2017). Much research has been done on identifying and prioritizing possible risk
indicators jeopardizing the process of osseointegration. The primary focus of research
efforts has been on smoking, diabetes, history of periodontal disease and history of
implant failure as possible risk indicators (see Table 2). Several other factors have also
been assessed, and they will be reviewed in subsequent sections. Sections 1.8.1 to 1.8.8
will review individual risk indicators for implant failure. Section 1.8.9 will review large-
scale studies showing how these individual risk indicators work together to impact
implant treatment outcomes.
Numerous terms are used in epidemiologic studies to assess an individual’s risk for
developing a disease. Risk factors are environmental, behavioural, or biologic factors that
increase the chances of an individual developing a disease. Risk indicators are probable
risk factors that have been identified through cross-sectional studies but have not been
confirmed by longitudinal studies. Risk predictors are markers that may be associated
with increased risk for the disease, but do not cause the disease (Fletcher and Fletcher
2005). However, the term “risk factor” is often used in the literature in a generic manner
even when a factor has not been confirmed to be a true risk factor. This research
however, aims to identify the risk indicators, as we believe this terminology would be
more appropriate to use.
1.8.1 Smoking
Smoking is a significant health hazard with a global prevalence of 1.22 billion individuals
although it has declined among the adult population since the 1980s. The numerous
health risks of smoking include neoplastic, vascular and respiratory diseases. Mortality
rates of smokers are reported to be three times higher than non-smokers resulting in an
average loss of at least a decade of life (Jha et al. 2013). Smoking continues to be
considered as a risk factor for implant treatment; however, this is less so with the current
moderately rough surface implants in comparison to the initially utilized machined
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(minimally rough) surface implants for which a significant association between smoking
and implant failure has been reported (HR: 0.8 vs. HR: 1.3 respectively) (Balshe et al.
2008). Smoking exposes the peri-implant tissues to nicotine, which negatively affects
wound healing by compromising fibroblast function and reducing collagen production
(Nociti et al. 2002; Cesar-Neto et al. 2003). Interference with chemotaxis and
phagocytosis of polymorphonuclear neutrophils as well as decreased immunoglobulin
production and functioning of lymphocytes also contribute to compromised wound
healing (Kenney et al. 1977). Generally, smoking compromises the osseointegration
process by decreasing the bone-to-implant contact and bone density in and around the
threaded area of the implants (Bezerra Ferreira et al. 2016). In a recent systematic review,
Moraschini et al. (2016) demonstrated that marginal bone loss and implant success are
negatively affected by smoking. They reported a statistically significant difference in the
marginal bone loss around implants (SMD: 0.49) and implant failure rates (OR: 1.96)
with both favoring non-smokers (Moraschini et al. 2016).
1.8.2 Diabetes
Diabetes mellitus is a chronic metabolic disorder that leads to chronic hyperglycaemia as
a result of the inability of the pancreas to produce sufficient insulin or inability of the
cells to effectively utilize the produced insulin. The prevalence of diabetes is more than
400 million individuals worldwide and is rising especially in middle- and low-income
countries. Diabetes causes micro- and macro-vascular complications resulting in
neuropathy, retinopathy, nephropathy and cardiovascular diseases. An estimated 1.6
million deaths have been directly attributed to this disease, and an additional 2.2 million
attributed to hyperglycaemia (WHO, Media Center Fact Sheet 2017). Hyperglycaemia
causes a delayed healing response secondary to lower concentrations of immune cells,
growth factors and cytokines as well as reduced collagen synthesis (Devlin et al. 1996;
Doxey et al. 1998). Therefore, diabetes impairs osseous wound healing, negatively
impacts osseointegration and decreases the removal torque of the implants (de Molon et
al. 2013). Diabetes mellitus, particularly if uncontrolled, is associated with an increase in
marginal bone loss around implants, but it does not significantly affect implant failure
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rates (Chrcanovic et al. 2014; Moraschini et al. 2016). A statistically significant
difference in marginal bone loss between diabetic and non-diabetic patients, favouring
non-diabetic patients has been reported (mean difference: 0.2), but no statistically
significant difference in implant failure rates between the two groups was noted (RR:
1.07) (Chrcanovic et al. 2014). Moraschini et al. (2016) also report similar results (mean
difference: 0.18, RR: 1.43).
1.8.3 History of Periodontitis
Periodontitis is defined as the presence of gingival inflammation at sites where
pathological detachment of collagen fibers from the cementum and the junctional
epithelium has resulted in an apical migration of the clinical attachment levels (Savage et
al. 2009). Despite a general decrease in prevalence of periodontitis, it still presents with a
prevalence of about 4.2% in the general population (Borrell and Talih 2012). The
relationship between history of periodontitis and implant failure is controversial. High
implant survival rates have been reported in patients with severe forms of periodontal
disease (Monje et al. 2014). Nonetheless, some authors (e.g., Sousa et al. 2016) reported
higher rates of implant loss and biological complications in patients with a history of
severe forms of periodontitis. In addition to an overlap between the causative agents for
peri-implant and periodontal diseases, a general association between oral microbiota
(plaque accumulation) and peri-implant mucositis, loss of marginal bone and loss of
implants has been described (Quirynen et al. 2002). The initiation of both periodontitis
and peri-implantitis depends on the presence of pathogen-containing biofilm, and a
similar gram-negative rich biofilm has been identified in periodontitis and peri-
implantitis (Heitz-Mayfield and Lang 2010). However, histopathological differences
between the two disease entities have also been reported (Berglundh et al. 2011).
1.8.4 History of Implant Failure
Evidence suggests that history of implant failure is a predictor for future failures. The
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odds of having a second implant removed are reported to be 1.3 times greater in patients
with a positive history of a failed implant (Weyant and Burt 1993). Similarly, Schwartz-
Arad et al. (2008) have reported survival rates of 77% in individuals with previously
failed implants. A systematic review by Quaranta et al. (2014) reported survival rates of
71-100% for implants placed in areas of previously failed implants and a survival rate of
83.7% for implants placed for the second time (third attempts) after failure of previous
implants; however, a high risk of bias in the underlying studies was identified. The
replaced implant is subject to higher failure rates because it may be exposed to the same
endogenous and exogenous risk factors that possibly led to the initial failure. It is
hypothesized that newer textured implant surfaces may offer optimized prognosis in
replacement of failed machined surface implants (Quaranta et al. 2014). Similarly,
Chrcanovic et al. (2017) reported a statistically significant lower survival rate for
implants replacing failed ones (73%) in comparison with implants placed for the first
time (94%) hypothesizing that a possible site-specific negative effect may be associated
with higher failure rates of replaced implants (Chrcanovic et al. 2017).
1.8.5 Occlusal Forces and Bruxism
Occlusal forces may exceed the mechanical or biological capacity of the implants or
prostheses, leading to ‘overload’ which manifests as mechanical complications or failure
of osseointegration (Isidor 2006). The association of bruxism with biological failure of
implants remains controversial, while the association of bruxism with mechanical
complications is well accepted. Evidence suggesting bruxism as a risk factor for
biological complications is limited and must be viewed in the context of poor quality of
the approaches to diagnose bruxism in the literature (Manfredini et al. 2014). When
associations with implant loss have been suspected, many studies report on implant
failure without specifying whether that includes implant loss due to implant fracture,
implant loss due to biological implant failure, or both (Zhou et al. 2016). Similarly, no
relationship has been found between the crown-to-implant ratio and peri-implant crestal
bone loss, biological complications or failure of implants (Blanes 2009), and this
strengthens the argument that occlusal forces may not be a major factor in implant failure.
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Nonetheless, the most recent systematic review on this subject by Zhou et al. (2016)
concluded that bruxism is a contributing factor to dental implant technical/biological
complications and plays a role in dental implant failure.
1.8.6 Surgical Experience
The presence of a learning curve for practitioners performing medical and dental
surgeries has been demonstrated in numerous studies including those focusing on dental
implant treatment (Sendyk et al. 2017). As surgeons perform more procedures and gain
experience, increased operative proficiency and shorter operating times can be expected
(Mayo et al. 2016). Sendyk et al. (2017) conducted a systematic review on the effect of
surgical experience of the operator on implant survival rate. The authors found that the
manner in which surgical experience was defined was critical to the results: surgical
experience merely based on specialty training did not significantly affect implant failure
rates, while surgical experience did significantly affect failure rates when experience was
defined as the number of placed implants. Although the results of the underlying studies
may have been skewed due to multiple confounding factors, the authors concluded that
surgeons who placed more implants presented with fewer failures than those who placed
fewer implants (Sendyk et al. 2017).
1.8.7 Medications
The effect of medications on implant failures have been investigated by several authors
including Carr et al. (2018) who found that corticosteroid use at the time of implant
placement was associated with a decrease in the risk of implant failure. The use of
selective serotonin re-uptake inhibitors (SSRIs), peri-operative use of non-steroidal anti-
inflammatory drugs (NSAIDs) and intake of proton-pump inhibitors have been reported
to be associated with an increased failure risk (Wu et al. 2014; Winnett et al. 2016;
Chrcanovic et al. 2017; Chappuis et al. 2018). On the other hand, antihypertensive
medications have been reported to be associated with an increase in survival rate of
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dental implants (Wu et al. 2016). Wu et al. (2016) assessed the effect of antihypertensive
medications on survival of dental implants in a retrospective cohort study. This study
assessed 1,499 implants in 728 patients, out of which 327 implants were placed in 142
antihypertensive drug users and 1,172 implants were placed in 586 nonusers. The authors
reported 0.6% failure rate in antihypertensive drug users and 4.1% in nonusers implying
that treatment with antihypertensive medications is associated with an increased survival
rate and decreased failure rate for dental implants due to positive effects on bone
formation, metabolism and remodeling (Wu et al. 2016).
1.8.8 Other Risk Factors
A two-part retrospective study by Eckert et al. (2001) described the survival of wide-
platform and wide-diameter implants and evaluated the risk factors associated with
implant survival. In this study, 85 posterior wide-platform implants in 63 patients showed
19% loss in the mandible and 29% loss in the maxilla. Failure of implants was defined as
loss or removal of the implant. They reported no relationship between implant length and
failure; however, existing root canal therapy with or without retrograde amalgam fillings
was associated with increase in implant failure risk. No specific patient-related factors
were reported in association with implant failure (Eckert et al. 2001).
Attard and Zarb (2002) studied implant treatment outcomes in patients with
hypothyroidism by examining 27 female patients with well-controlled primary
hypothyroidism and 29 control patients without hypothyroidism who were matched based
on age, gender, location of the implants, prosthesis type and dental status of the opposing
arch. The outcome suggested that a positive history of controlled hypothyroidism had no
effect on success or failure of dental implants (Attard and Zarb 2002). Based on the
results of this study and the general absence of literature supporting history of
hypothyroidism as a possible implant treatment risk factor, this condition was not
assessed in the present study.
Investigators have assessed the influence of genetic factors on implant treatment
outcomes since dental implant failures tend to cluster in subsets of patients. Host
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immune-inflammatory responses and polymorphism of inflammatory mediators such as
IL-1β have been considered in a preliminary manner (Dirschnabel et al. 2011). Current
research does not appear to offer strong support for a genetic basis to implant failure
(Santiago et al. 2018), and this line of enquiry was not pursued in the current study.
1.8.9 Large-Scale Studies Examining Risk Indicators for Implant Failure
Several large-sample studies, including those reporting early experiences with dental
implants at the University of Toronto, were reviewed to identify risk indicators for
implant failure reported in the literature. Three prospective Toronto studies were
conducted by Zarb and Schmitt (1989, 1990(I, II, III)) on the longitudinal effectiveness of
the 274 Branemark system implants placed in 49 arches (43 mandibles, 6 maxillae). After
4-9 years of follow-up, 89.05% success rate was reported. The Toronto studies suggested
that failure of implants to osseointegrate, as determined at stage II surgery, was most
often iatrogenic in nature and related to one or both of the following factors: (1) over-
instrumentation of the bone site leading to lack of adequate primary stability and (2)
inadequate implant length to engage the mandible’s inferior cortical plate when
unfavorable bone quality was present. Failure to osseointegrate at stage I surgery was
reported to result from faulty surgical technique, deviation from the prescribed
sterilization and surgical protocols, or the presence of compromised local blood supply
(Zarb and Schmitt 1989, 1990(I, II, III)).
A multicenter study by Albrektsson (1988) on the success of dental implants reported the
3-year success rate of 1,029 assessed implants to be 96.02%. Bone grafting and
irradiation were considered as risk factors for failure of dental implants and were
assessed. The following results were reported: 19 implants were inserted in grafted
mandibles with only one failure, giving a l- to 5-year success rate of 94.74%. The l- to 5-
year success rate of implants inserted in the irradiated mandible was l00% based on 21
implants. In the grafted maxillae, 112 implants were inserted, 38 of which failed, giving a
l- to 5-year statistic of 66.08%. The 1- to 5-year success rate of implants inserted in the
irradiated maxillae was 100% based on only 10 inserted implants. In the mandible,
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grafted and irradiated patients showed a 1- to 5-year success rate of 94.74% and 100%. In
the maxillae, grafted patients showed a success rate of 66.1% (n = 112) and irradiated
patients had a 100% success rate (n = 10) (Albrektsson 1988).
Retrospective multi-clinic study by Albrektsson et al. (1988) assessed implant treatment
outcomes of 8,139 implants placed in 14 private clinics. In the mandibles, 4,907 implants
were placed in 918 patients (average of 5.35 implants/patient), out of which 52 failures
were recorded, indicating a success rate of 98.88%. In the maxillae, 3,089 implants were
placed in 723 patients (average of 4.27 implants/patient), out of which 218 failures were
recorded, indicating a success rate of 92.95%. In grafted and irradiated mandibles, out of
334 implants, 3 failures were reported after 5-8 years of follow-up, indicating a success
rate of 99.1% and in 106 implants in the grafted and irradiated maxillae followed-up for 5
to 7 years a success rate of 84.9% was identified. As for irradiated jaws, in the mandible
none of the 56 implants and in the maxilla 3 of the 16 implants failed. In grafted jaws, 12
failures of 71 implants in the maxilla were reported. The following success rates were
reported: 100% in grafted and irradiated mandibles, 83.1% in the grafted maxillae,
81.25% in irradiated maxillae. As for the time of failures, 78.8% of failures in the
mandible and 82.9% of failures in the maxilla occurred during the first year after implant
placement. The research team did not remove splinted multi-unit implant-supported
prostheses to assess mobility of individual implants.
Adell et al. (1990) assessed 759 edentulous jaws of 700 patients, encompassing 4,636
implants, which were followed-up for 24 years in the University of Goteborg. In this
study more than 95% of maxillae had continuous prosthesis stability at 5 and 10 years,
and at least 92% of maxillae had continuous prosthesis stability at 15 years. The
estimated survival rates of implants in the maxilla were 92% at 5 years, 81-82% at 10
years, and 78% at 15 years. In the mandible the numbers were 91-99% at 5 years, 89-98%
at 10 years, and 86% at 15 years (Adell et al. 1990). The location of implant placement
was determined to be a predictor for failures. Similarly, Jemt (1991) studied the failures
and complications of 2,199 implants placed in 391 edentulous jaws and found a success
rate of 99.5% for prostheses and 98.1% for the implants. More failures were noted in the
maxilla (2.9%) than in the mandible (0.4%) (Jemt 1991). Implants that were removed due
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to clinical mobility or significant compromise in osseointegration were considered in this
study.
A large-scale retrospective study by Friberg et al. (1991) in Gothenburg, Sweden,
reported exclusively on early implant failures of 4,641 Branemark implants in 943 jaws
of 889 patients. This study reported 1.5% failure rate, mostly in completely edentulous
maxillae and with shorter implants (less than 7 mm), and most failures were reported at
abutment connection stage. This study does not provide any statistical analysis and is
reported in a descriptive format. In 39% of the failure cases, a bone defect or limited
amount of bone was reported in the surgical records of the patients, and in 32% of the
failure cases, implants were reported to have been placed in extremely soft bone and/or
no initial stability was achieved. The quality and shape of bone were stated to be the most
important factors in early implant failures (Friberg et al. 1991).
The study by Buser et al. (1997) on the long-term prognosis of 2,359 non-submerged ITI
implants in 1,003 patients, revealed 13 early failures with 2,346 implants meeting the
predefined success criteria and resulting in an 8-year cumulative survival and success
rates of 96.7% and 93.3% respectively. In total, non-submerged ITI implants maintained
a high success rate of above 90% in the assessed clinical centers for the 8-year
observation period. Higher success rates were observed with mandibular implants and
implants longer than 8 mm. In the mandible, 94.1% and 95.4% cumulative success rates
were observed in the anterior and posterior regions respectively. After 8 years, 86.7% and
87.8% cumulative success rates were observed in the anterior and posterior regions of
maxillae respectively (Buser et al. 1997).
A retrospective study by Eckert and Wollan (1998) conducted in the Mayo clinic
described the implant survival of 1,170 implants in 631 partially edentulous patients. This
study excluded completely edentulous and craniofacial patients. Of these implants, 651
were placed in the mandible supporting 351 prostheses and 519 were placed in the
maxilla supporting 280 prostheses. The study assessed several complications including
implant loss, implant fracture, retaining screw loosening and screw fracture as well as
single implant restoration cement failure; hence, the main focus of this study was not on
biological performance of the implants. It was reported that implant survival was
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independent of anatomic location, and clinical performance was improved by design
enhancements in restorative implant components. Absolute survival rates were reported
to be 96.3% in the maxillae and 95.4% in the mandibles. It was also noted that implant
loss in the anterior region tends to occur early while implant loss in the posterior regions
tends to occur relatively later; hence, most late implant failure cases tended to occur in
the posterior region (Eckert and Wollan, 1998). Noack et al. (1999) conducted a long-
term longitudinal study on survival of 1,964 implants in 883 patients over 16 years. They
also reported higher success rates for mandibular implants than those placed in the
maxillae (83% vs. 72%). A pre-prosthetic implant loss of 1.9% and post-prosthetic
loading implant loss of 4.3% were reported. Plaque, time of placement, location of
implants and the extent of the peri-implant bone resorption were reported to impact on
the long-term survival of the implants (Noack et al. 1999). Similarly, a longitudinal study
by Brocard et al. (2000) on 1,022 ITI implants in 440 patients from 10 private practice
clinics evaluated implant treatment outcomes and found an early failure rate of 1.4%, a
cumulative survival rate of 95.4% at the end of 5 years, and a cumulative survival rate of
92.2% at the end of 7 years.
Retrospective cohort study by Alsaadi et al. (2007) on patients receiving implant
treatment at the Catholic University, Leuven between 1982 and 2003 assessed local and
systemic factors associated with implant failures. The authors examined 2004 consecutive
patients treated with 6946 Brånemark implants and reported a failure rate of 3.6%. Many
of the assessed factors were found not to be associated with an increased incidence of
early failures, and these factors included cardiac and gastric diseases, controlled diabetes
type II, coagulation problems, hypertension, hypo- or hyperthyroidism,
hypercholesterolemia, asthma, radiotherapy of the treatment site, claustrophobia,
antibiotic therapy, antidepressants and corticosteroid medications. By contrast, Crohn’s
disease, osteoporosis, smoking, implant-related factors (length, diameter and location)
and vicinity with the natural dentition were significantly associated with early implant
failures (Alsaadi et al. 2007).
Retrospective assessment of 308 patients treated with 674 single-stage implants was
conducted by Carr et al. (2003) from 1993 to 2000 to assess the long-term clinical
performance of single-stage implant prostheses in Mayo clinic. A survival rate of 97%
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and a complication rate of less than 4% were reported for the 78 months of the
retrospective study. The majority of the patients were women (55%), and 50% of the
subjects were between 38 and 56 years of age. Most failures were in the mandibular
posterior region, and fewest failures were identified in the anterior mandible. The results
of this study revealed that implant length, abutment type and prosthesis type did not
contribute significantly to implant failure after adjustment for age and sex. Implants with
4.8 mm diameter failed 3.4 times more than those of 4.1 mm diameter, and implants
placed in augmented sites were 5 times more likely to fail than those placed in non-
augmented bone.
A retrospective study by Moy et al. (2005) on risk factors for implant failures evaluated
4,680 implants in 1,140 patients and reported an overall implant failure rate of 4.93% in
the mandible and 8.16% in the maxilla. Implant failure was defined as any condition
leading to removal of the implant, such as mobility, pain, infection, fracture, intolerable
paraesthesia, anesthesia or dysesthesia, and radiographic bone loss more than 50%. This
study identified age, smoking, diabetes, head and neck radiation, and postmenopausal
estrogen therapy as being strongly associated with the risk of implant failure. By contrast,
gender, hypertension, coronary artery disease, pulmonary disease, corticosteroid therapy,
chemotherapy and not being on hormone replacement therapy for postmenopausal
women were found not to be associated with significant increase in implant failure.
Smoking was reported to be associated with a 1.56 relative risk of failure. No medical
risk factor was identified as an absolute contraindication to implant placement (Moy et al.
2005).
A recent retrospective cohort study on the general incidence and determinants of dental
implant failures by Hickin et al. (2017) evaluated 6,129 implants in 2,127 patients and
identified 179 failed implants (2.9% of all implants placed) in 120 patients (5.6% of those
who received implants); hence, annual incidences of failure of 0.8% at the implant level
and 1.6% at the patient level were reported. Of the 120 patients with failures, 85 (70.8%)
experienced failure of a single implant and 35 (29.2%) experienced failure of 2 or more
implants. Patients who received pre-surgical prophylactic antibiotics experienced fewer
implant failures (1.7% vs. 3.7%), and patients receiving removable or fixed provisional
prostheses had higher failure rates (OR: 2.8 and 3.7 respectively) (Hickin et al. 2017).
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Removed dental implants registered in the Finnish Dental Implant Register from 1994 to
2012 were assessed by Antalainen et al. (2013) to evaluate the effect of patient
characteristics on failure of dental implants. The report indicated an overall low implant
removal rate. From 1994 to 2012, a total of 198,538 dental implants (51 different types)
were placed, out of which 3,318 (1.7%) were removed – 1,856 (1.8%) from maxillae and
1,462 (1.5%) from mandibles – with a median removal time of 247 days. More than half
of the implant placements and the majority of the removal operations involved only one
implant. Removal rates were almost equal in the maxilla and mandible. The authors
reported that 93.3% of the used implants had been 10 mm or longer and that shorter
implants (8 mm or less) were more frequently removed than longer implants. It was also
reported that IMZ implants were most frequently removed and Branemark Nobel Direct
implants were least frequently removed with removal rates of 8.5% and 0.6%
respectively. The two most commonly used implant systems in Finland (60.9% of all
implants placed), Straumann and Astra, demonstrated the same removal rate of 1.2%. In
assessment of the implant removal time after placement, 1/3 of the removals occurred
during the first 142 days, 1/3 between 142 and 929 days, and the final 1/3 beyond 929
days. The advantage of this study is that it represents a very large, comprehensive and
government mandated country-level data set; however, the data is self-reported and,
hence, is susceptible to underreporting and error (Antalainen et al. 2013). In the context
of implant failures, it is also relevant to evaluate failure of other osseointegrated implants
such as zygomatic implants and bone-anchored hearing devices. In a systematic review
on zygomatic implants, Chrcanovic and Abreu (2013) pooled results from 42 studies on
1,145 patients with 2,402 zygomatic implants to calculate the cumulative survival rate of
zygomatic implants to be 96.7% over a 12-year period. They reported that most
zygomatic implant failures were detected at the abutment connection phase, within the
first 6-month interval of implant placement which parallels results observed with
conventional implants (Chrcanovic and Abreu 2013).
A retrospective study by Larsson et al. (2015) on 571 patients who received bone-
anchored hearing devices and were followed-up for 32 years (median of 6.6 years)
reported 46 failures (98.2%). This cohort of patients had a total of 763 implants inserted
and 18% of the implants were lost: 109 (14%) due to loss of osseointegration, 21 (3%)
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due to trauma and 11 (1.5%) were electively removed. Some patients had reinstallation of
implants (27 patients had 1 reinstallation, 5 patients had 2 reinstallations, 9 patients had 3
reinstallations and 5 patients had 4-6 reinstallations). A cluster phenomenon was detected
since out of the 141 lost implants, 78 (55%) were lost in 19 patients (2 or more implant
losses) indicating that majority of losses (55%) took place in a small group of patients
with multiple implant failures. A total of 59 implants were lost: 28 due to loss of
osseointegration and 22 due to direct trauma to the implant. Time to implant loss varied
from 3 to 18 years (mean of 9 years). Multiple implant failures of bone-anchored hearing
devices were more prevalent in male subjects and slightly younger patients. Similar to
dental implants, early losses of extraoral implants appear to be associated with
inappropriate surgical techniques, and late losses with patient-related factors such as
smoking, diabetes, hygiene, or age (Larsson et al. 2015).
Based on the studies reviewed in section 1.8, possible risk indicators for implant failure
were assembled and then compared against possible factors associated with failure of
multiple dental implants (see section 1.11).
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Table 2: Studies indicating magnitude of effect and biological plausibility of primary factors (smoking, diabetes, history of periodontitis, and history of implant failure) associated with implant failure
Risk factors Magnitude of effect (RR, 95% CI) Biologic plausibility Comments/notes
Smoking -negative affect of smoking on marginal bone loss around implants (SMD: 0.49, 95% CI: 0.7-0.9, p= 0.02) and on implant success rates (OR: 1.96, 95% CI: 1.68-2.3, p<0.00001) (Moraschini et al. 2016)
-nicotine à negative effect on wound healing by compromised fibroblast function + reduced collagen production (Nociti et al. 2002; Cesar-Neto et al. 2003)
-interference with chemotaxis and phagocytosis of polymorphonuclear neutrophils, decreased immunoglobulin production and functioning of lymphocytes à compromised wound and osseous healing (Kenney et al. 1977)
-implant failure rates of 11.28% in smokers and 4.76% in non-smokers (Bain & Moy 1993)
-major risk factor for implant failure (except in posterior Md)
Diabetes -statistically sig difference in marginal bone loss between diabetic and non-diabetic pts (mean difference: 0.2, 95% CI: 0.08-0.31, p=0.001) favouring non-diabetic patients -no statistically sig difference in implant failure rates (RR: 1.07, 95% CI: 0.8-1.44, p=0.65) (Chrcanovic et al. 2014)
-hyperglycaemia à lower concentrations of immune cells, growth factors, cytokines and reduced collagen synthesis à delayed healing response (Devlin et al. 1996; Doxey et al. 1998)
-impaired osseous wound healing à negative impact on osseointegration + decrease in the removal torque of the implants (de Molon et al. 2013)
-presence of multiple uncontrolled confounding factors in the underlying studies
-no statistically sig difference in implant failure rates between type II diabetic and non-diabetic pts (RR: 1.43, 95% CI: 0.54-3.82, p=0.47) or between type I diabetic and non-diabetic pts (RR: 3.65, 95% CI: 0.33-40.52, p=0.29) or between type I and II diabetic pts (RR: 1.56, 95% CI: 0.62-3.91, p=0.34) -statistically sig difference in marginal bone loss between pts with type II diabetes and non-diabetic pts, favouring non-diabetic pts (MD: 0.18, 95% CI: 0.14-0.21, p<0.00001) (Moraschini et al. 2016)
-number of implant failures did not differ in diabetic and non-diabetic groups
History of periodontitis
-similar implant survival rates with generalized aggressive periodontitis vs. healthy periodontium (RR: 0.96, 95% CI: 0.91-1.01, p=0.14) and with generalized aggressive periodontitis vs. chronic periodontitis (RR: 0.94, 95% CI: 0.87-1.01, p=0.09) -overall RR: 4 for aggressive periodontitis vs. healthy periodontium and overall RR: 3.97 for aggressive periodontitis vs. chronic periodontitis (Monje et al. 2014)
-same microbiota present in periodontal disease, especially aggressive and severe periodontitis, may play a role in failure of implants
-pre-existing intraoral ecological conditions may influence the formation of biofilms on implants (Mombelli et al. 1987)
-residual pockets à niche for
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Risk factors Magnitude of effect (RR, 95% CI) Biologic plausibility Comments/notes
-RR: 1.03 (95% CI: 1.02-1.04) (p <0.001) for periodontitis (especially aggressive) vs. healthy periodontium à possible association with a statistically sig (not clinical) higher risk for long-term implant survival (Wen et al. 2014)
microbiota accumulation à peri-implant disease (Mombelli et al. 1987)
-limited strength of underlying evidence (controversial nature of the presumed association)
History of implant failure
-odds of having a second implant removed: 1.3 times greater in pts with a positive history of a failed implant (Weyant and Burt 1993)
-genetic and biological susceptibility of the pt to cluster phenomenon
-replaced implant subject to same risk factors leading to initial failure and placed in an individual with increased risk of implant failure (Quaranta et al. 2014)
-site-specific negative effect associated with higher failure rates (Chrcanovic et al. 2017)
-implant survival rates in individuals with previously failed implants: 77% (Schwartz-Arad et al. 2008)
-71-100% survival rates for implants placed in areas of previously failed implants and 83.7% for implants placed for the second time (third attempt) (Quaranta et al. 2014)
-high risk of bias in underlying studies
-statistically sig difference between implant survival for implants placed for the first time (94%) and those replacing failed ones (73%) (p=0.032) (Chrcanovic et al. 2017)
Abbreviations: CI (confidence interval), RR (risk ratio), pt (patient), sig (significant)
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1.9 Management of Implant Failures
Management of implant failures, especially late failures, involves removal of the failed
implant(s) and further management depending on the appropriate prosthodontic treatment
plan. The four primary determinants of the appropriate prosthodontic management are:
• number, location and distribution of the remaining osseointegrated
implant(s) (if any) is the main determinant of the future prosthodontic treatment
plan
• prosthesis design: fixed or removable, the number of units and characteristics of
the prosthesis
• method of prosthesis retention: screw- or cement-retained
• patient’s desires and expectations.
1.10 Timely Identification of Failed Implants
Timely identification of implant failure is critical to avoid further complications at the
site of the failed implant, at other implant sites, and at the prosthesis level. Implant failure
increases the load on the remaining implants supporting a multi-unit prosthesis increasing
the risk of biomechanical complications at the level of the retaining screw (screw
loosening and screw fracture), the prosthesis (prosthesis loosening, dislodgement or
fracture), and the implant (implant fracture, bone loss and implant failure). The
unfavourable load distribution on remaining implants supporting a multi-unit prosthesis
due to failure of implants is reported to result in the loss of implant-supported prostheses
in 1.3% of patients (Jemt and Hager 2006). Continued presence of a failed implant in the
jaw – as may occur with delayed diagnosis of the failure – increases the risk of further
bone loss and infection. With significant progression of bone loss, pathologic jaw fracture
may occur which is a rare but serious complication in the mandible (Albrektsson et al.
1988; Mason et al. 1990).
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1.11 Multiple Implant Failures (Cluster Phenomenon)
Multiple implant failure (also referred to as cluster failure) is the failure of multiple
implants within the same patient. Multiple implant failure has a significant negative
impact on the status or prognosis of the prosthodontic rehabilitation and may compromise
prognosis of remaining implants. Limited information is available on the epidemiology
and etiology of this phenomenon, but some authors suspect that implant losses cluster in
high-risk individuals. One study suggests that 1.3% of patients with an edentulous
maxilla lose their implant-supported full-arch fixed dental prostheses within 3 years of
insertion due to multiple implant failure (Jemt and Hager 2006).
Implant failure, especially failure of multiple implants in the same individual, is a patient-
relevant outcome as it directly relates to the success of the prosthesis or the ability of the
dentist to deliver the prosthesis. Failure of multiple implants in an individual patient may
result in the loss of the prosthesis and treatment failure with a significant psychological,
biological and financial burden on the patient. Furthermore, multiple implant loss may
compromise the quantity of the residual alveolar ridge complicating future implant care.
A comprehensive review of the literature identified four retrospective cohort studies
reporting on multiple implant failure (see Table 3).
Weyant and Burt (1993) performed a retrospective review on 598 patients with 2,098
implants over a 6-year time frame. Clustering of implant removals within patients with
multiple implants was reported: 81 implants in 45 patients were removed, resulting in an
average of 1.8 implant removal per “failed” case. This study did not report on the implant
brand or prosthesis design. The authors suggested that implant design, patient age and
location of implant in the arches did not affect implant failure rates.
Ekfeldt et al. (2001) conducted a retrospective study on 54 patients with completely
edentulous maxillae who received either a fixed prosthesis or an overdenture supported
by at least 4 implants during an 8-year time frame. The study group (half of the patients)
had suffered the loss of at least half of their implants, and the other half (the control
group) had no implant failures. The study aimed to determine factors associated with
multiple implant failure in the maxilla. The results indicated that factors associated with
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failure of multiple implants (defined in this study as loss of at least half of the placed
implants) were quality and quantity of bone, bruxism, diabetes, osteoporosis, personal
grief, depression and loading problems. Attribution of implant failure to factors such as
infection and bone overheating was based on the professional opinion of the private
practice clinicians who treated the study patients. The clinicians suggested that important
factors contributing to cluster failures were lack of bone support, suboptimal bone
quality, heavy smoking habits and bruxism.
Jemt and Hager (2006) conducted a retrospective study of 1,267 consecutively treated
patients aiming to identify patients who had lost their definitive fixed implant-supported
prosthesis in the maxilla within the first 3 years of prosthesis insertion due to loss of
supporting implants. They reported that 1.3% of the patients had lost their maxillary
implant-supported prostheses as a result of implant loss and concluded that bone quantity,
smoking habits and history of periodontitis have a significant impact on increased
implant failure risk. The authors noted that in some patients implant failures started
predominantly in one quadrant, resulting in an unfavorable distribution of the remaining
implants. This indicates that cluster implant failure may be the result of multiple factors
some of which are responsible for failure of the first few implants (e.g., poor bone
quality) while others play a role in the failure of remaining implants (e.g., loading
factors).
Chrcanovic et al. (2017) conducted a retrospective cohort study in Sweden on 1,406
patients with at least 3 implants. Among a total of 8,337 implants, 592 failures were
reported. Sixty-seven patients (4.8%) experienced cluster failures accounting for 56.8%
of all failures. The authors reported that less than 5% of the patient population accounted
for 56% of all implant failures. Their research suggested that intake of antidepressants
and medications to reduce the gastric acid production, smoking, bruxism, machined
surface implants, short implants, poor bone quality and smoking were potential risk
factors for dental implant failures. Greater percentage of implants were placed in jaw
bones with limited volume (types D and E) and limited quality (types 3 and 4) in the
cluster failure patients. Statistically significant odds ratio for implant failure at the
patient-level was found with the intake of antidepressants, intake of medications to
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reduce gastric acid production, intake of antithrombotic medications, smoking and
bruxism (see Table 3).
Table 3: Summary of cohort studies on multiple implant failures
Study (author/year)
Population (N)
Follow-up
duration
Failure Multiple implant failure
Definition of multiple
implant failure
Inclusion criteria
Comments / notes
Weyant and Burt (1993)
598 pts
2,098 implants
1987-1993 (6y)
-81 implants 45 pts: 1.8 implants removed per failed case, single implant failure rate: 4.9%, crude implant survival rate: 96.1% -OR of implant removal given one was already removed: 1.3
-survival rate during maximum of 5.6y:
-implant-specific: 89.9%
-pt-specific: 78.2%
-loss of more than one implant
-implants with at least one post-surgical FU
-data from department of Veterans Affairs (VA) dental implant registry (since 1987), comprehensive dental implant registry
Ekfeldt et al. (2001)
54 pts
301 implants
1988-1996 (8y)
no information provided
-128/151 in study group (43% before loading (early), 57% after loading (late)
-63% of late failures in first year of loading
loss of at least half the implants
-multiple implant failure -complete ed in Mx, ISFCDP or ISOD (at least 4 implants)
-factors of importance in cluster phenomenon: lack of bone, heavy smoking, bruxism
-1.8 mm longer implants in the group with no implant failure
Jemt and Hager (2006)
1,267 total pts, 17 met inclusion criteria
1988-2000 (12y)
no information provided
-79/102 in study group (3y survival rate: 22.5%)
-1.3% of ed pts provided with ISFCDP in Mx, lost prosthesis during first 3y after placement -4/114 in control group (3y survival rate: 95.7%)
loss of Mx ISFCDP within 3y of insertion due to loss of implants
-multiple implant failure -ed Mx, ISFCDP in Mx
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Study (author/year)
Population (N)
Follow-up
duration
Failure Multiple implant failure
Definition of multiple
implant failure
Inclusion criteria
Comments / notes
Chrcanovic et al. (2017)
1,406 pts
8,337 implants
=<1 y to >20 y (up to 34y)
766 pts had 1 implant, 17 failures (2.22%), not included in study
498 pts had 2 implants (996 implants), 37 failures (3.71%) not included in study
1,406 pts had 3 or more implants à 592/8,337 implant failures, 67 pts (7.10% of implants, 4.77% of pts) with cluster failures (331/620) accounting for 56.8% of implant failures
antidepressants: OR 3.694 (95% CI: 1.889-7.223, p<0.001), medications for gastric acid reduction: OR 2.246 (95% CI: 1.105-5.456, p<0.027), smoking: OR 2.683 (95% CI: 1.493-4.822, p<0.001), bruxism: OR 6.065 (95% CI: 2.901-12.681, p<0.001)
failure of at least 3 implants
-pts with at least 3 implants
-ISSC, FPDP 2-6 units, FPDP 7-10 units, IS-FCDP, IS-OD
-implant failure: signs and symptoms leading to implant removal
-antidepressants and bruxism: possible negative factors with statistically sig effect at the pt-level analysis
-machined and short implants, poor bone quality, pt age, intake of medications to reduce gastric acid production, smoking and bruxism: statically sig effect at the implant-level analysis
Abbreviations: CI (confidence interval), ed (edentulous), FPDP (fixed partial dental prosthesis), ISFCDP (implant-supported fixed complete dental prosthesis), ISFDP (implant-supported fixed dental prosthesis), ISOD (implant-supported overdenture), ISSC (implant-supported single crown), Mx (maxilla), OD (overdenture), OR (odds ratio), pt (patient), SD (standard deviation), sig (significant), y (year[s]).
Quality assessment was undertaken for the four cohort studies focusing on multiple
implant failure utilizing the Newcastle-Ottawa Scale (NOS) for assessment of cohort
studies (The Ottawa Hospital, Research Institute 2019). This checklist assesses the
quality of each study based on the selection of the exposed and non-exposed groups,
assesses comparability of the groups and exposure, and provides an overall assessment of
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the methodologic quality of each study. Each study was assigned a maximum of one star
for each sub-category within the selection and exposure categories as well as a maximum
of two starts for the category of comparability. The subcategories within the selection
category were representativeness of the exposed group, selection of the non-exposed
cohort, ascertainment of exposure and demonstration that outcome of interest was not
present at the start of the study. The comparability category focused on comparability of
the groups on the basis of the design or analysis (controlled for confounders). The
subcategories within the exposure category were assessment of outcomes, adequacy of
follow-up time for outcome to occur, and adequacy of follow-up of cohorts. A study
would be considered as good quality if it were assigned 3 or 4 stars in the selection
category, 1 or 2 stars in the comparability category, and 2 or 3 stars in the exposure
category. A study would be considered of fair quality if it were assigned 2 stars in the
selection category, 1 or 2 stars in the comparability category and 2 or 3 stars in the
exposure category. A study would be of poor quality if it were assigned 0 or 1 star in the
selection category or 0 stars in the comparability category or 1 star in the exposure
category.
The results of the Newcastle-Ottawa Scale assessment revealed that all four studies on
multiple implant failure presented with quality scores ranging from 5 to 7 (out of a total
of 9). Three studies had a low-to-medium risk of bias due to relatively rigorous
methodology, and one study (Jemt and Hager 2006) had a high risk of bias. The results of
the risk-of-bias assessment are presented in Table 4.
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Table 4: Newcastle-Ottawa Scale assessment of the cohort studies on multiple implant failure
Study (author/year)
Selection category and score (*) Comparability category and
score (*)
Exposure category and
score (*)
Total score (*)
and risk of bias
assessment
Weyant and Burt (1993)
exposed cohort is not representative of the average population
study population is from veterans (multiple medical problems and high rates of alcohol and tobacco use)
findings may be too conservative on implant survival
non-exposed cohort is drawn from the same community as exposed cohort (department of Veterans Affairs)*
exposure ascertained via secure record (dental records)*
demonstration that outcome of interest was not present at start of the study*
(3*)
comparable cohorts on the basis of the design or analysis*
(1*)
assessment of outcome based on dental records*
FU possibly sufficient (maximum 5.6y)*
statement of adequacy of FU of cohorts*
(3*)
7* (=3+1+3)
low-to-medium risk of bias
Ekfeldt et al. (2001)
exposed cohort is truly representative of the average population*
non-exposed cohort is drawn from the same community as exposed cohort* (completely ed in Mx, ISFCDP or OD on 4 implants)
matched in terms of age, gender, number of inserted implants and time of implant placement (not the same surgeon)
all identified through recall systems in different clinics
exposure ascertained via secure record (dental records)*
clear demonstration that outcome of interest was not present at start of the study*
(4*)
comparable cohorts on the basis of the design or analysis*
(1*)
assessment of outcome based on records*
FU possibly sufficient (8y)*
no statement of adequacy of FU of cohorts
(2*)
7* (=4+1+2)
low-to-medium risk of bias
Jemt and Hager (2006)
exposed cohort is truly representative of the average population*
non-exposed cohort is drawn from the same community as exposed cohort
control group has less bone resorption and sig difference in distribution of
assessment of outcome based on records and clinical judgment of experts*
5* (=4+0+1)
high risk of bias
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randomly selected
undergone surgery in the same week at the study match (completely ed in Mx, ISFCDP)
matched in terms of age, gender, time of implant placement*
exposure ascertained via secure record (dental records) and clinical judgment of experts*
clear demonstration that outcome of interest was not present at start of the study*
(4*)
short and long implants (more short implants in study group)
higher awareness of failure risk in the pre-surgical discussion in study group
(0*)
FU not sufficient for late failures to occur (3y)
clear statement on 5 cases lost to FU in control group
may be likely to introduce bias
(1*)
Chrcanovic et al. (2017)
exposed cohort is truly representative of the average population*
non-exposed cohort is drawn from the same community as exposed cohort (received implant treatment at the same specialty clinic)*
exposure ascertained via secure record (dental records)*
clear demonstration that outcome of interest was not present at start of the study*
(4*)
comparable cohorts on the basis of the design or analysis*
(1*)
assessment of outcome based on records*
FU sufficient (more than 20y of FU)*
no statement of adequacy of FU of cohorts
(2*)
7* (=4+1+2)
low-to-medium risk of bias
Abbreviations: ed (edentulous), FU (follow-up), ISFCDP (implant-supported fixed complete dental prosthesis), Mx (maxilla), OD (overdenture), OR (odds ratio), pts (patients), sig (significant), y (year[s]).
An asterisk (*) next to a statement indicates that it fulfills one of the predefined criteria of the NOS scale.
Overall, clinical studies on multiple implant failure are limited and vary tremendously in
methodology, patient population, as well as definitions of implant failure and multiple
implant failure. Drawing of definitive conclusions on the epidemiology or etiology of
multiple implant failure is difficult, and no definitive etiologic factors for multiple
implant failure have been identified in the existing literature. The literature is unclear
whether the risk indicators for implant failure and multiple implant failure are the same
and whether risk indicators for single implant failure are the same as risk indicators for
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multiple implant failure (see Table 5). A larger cohort study assessing a greater number
of implants and patients with a greater methodological rigor (longer follow-up, precise
definition of multiple implant failure and presence of a comparison group) would
overcome some of the limitations in the existing studies on multiple implant failure. The
current study provides the first comparative analysis of risk indicators between single and
multiple dental implant failures.
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Table 5: Factors associated with implant failure and failure of multiple implants
Factors associated with implant failure Factors associated with failure of multiple implants
smoking (Bain and Moy 1993; Moy et al. 2005; Balshe et al. 2008; Bezerra Ferreira et al. 2016; Moraschini et al. 2016) smoking (Chrcanovic et al. 2017)
diabetes (Moy et al. 2005; de Molon et al. 2013; Chrcanovic et al. 2014; Moraschini et al. 2016) diabetes (Ekfeldt et al. 2001)
history of periodontitis (Wen et al. 2014; Sousa et al. 2016) history of periodontitis (Jemt and Hager 2006)
occlusal forces and bruxism (Zhou et al. 2016) occlusal forces and bruxism (Ekfeldt et al. 2001; Chrcanovic et al. 2017)
use of proton-pump inhibitors (Chrcanovic et al. 2017) use of proton-pump inhibitors (Chrcanovic et al. 2017)
use of antidepressants (Chrcanovic et al. 2017) use of antidepressants (Chrcanovic et al. 2017)
suboptimal bone quality and quantity (Zarb and Schmitt 1989, 1990(I, II, III); Friberg et al. 1991)
suboptimal bone quality and quantity (Ekfeldt et al. 2001; Jemt and Hager 2006; Chrcanovic et al. 2017)
location of implant placement (maxilla>mandible) (Adell et al. 1990; Jemt 1991; Buser et al. 1997; Noack et al. 1999)
location of implant placement (maxilla>mandible) (Jemt and Hager 2006)
implant surface (machined) (Alsaadi et al. 2006) implant surface (machined) (Chrcanovic et al. 2017)
surgical experience (Sendyk et al. 2017)
inadequate primary stability (Zarb and Schmitt 1989, 1990(I, II, III))
history of implant failure (Weyant and Burt 1993; Schwartz-Arad et al. 2008; Quaranta et al. 2014; Chrcanovic et al. 2017)
bone augmentation (Albrektsson 1988; Carr et al. 2003)
radiation to the head and neck (Albrektsson 1988; Moy et al. 2005)
use of NSAIDs (Winnett et al. 2016)
use of SSRIs (Wu et al. 2014 )
implant length (Friberg et al. 1991; Buser et al. 1997)
implant diameter (Carr et al. 2003)
plaque (Noack et al. 1999)
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1.12 Implications of Research
This study may have implications for directing future clinical research and education as
well as for improving patient treatment outcomes. Recognition of possible factors
associated with failure of multiple implants as well as provision of a comparison between
such factors for single and multiple implant failures in patients with multiple dental
implants can lead to better understanding of risk assessment, diagnosis and prevention of
biologic implant complications. With future research on the topic, improvements in
patient selection process, treatment planning and execution can lead to improved
treatment outcomes.
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2 Purpose and Statement of Problem
2.1 Purpose
The purpose of this research is to identify and compare possible risk indicators associated
with failure of multiple versus single dental implants
2.2 Statement of the Problem
Osseointegrated dental implants have revolutionized the world of dentistry by providing
well-documented lasting improvement in the quality of life of completely and partially
edentulous patients (Adell et al. 1990). Despite high predictability, implant treatments
can experience complications including implant failure. The etiology of dental implant
failure is imperfectly understood (Esposito et al. 1998(II)). Failure of multiple dental
implants in the same patient also may occur and is a rare but serious complication with
significant morbidity. However, research focusing on multiple implant failure and
understanding of this phenomenon is limited, resulting in a lack of evidence-based
clinical guidelines in this area (Ekfeldt et al. 2001). A well-designed large retrospective
study evaluating factors associated with multiple implant failure and providing a
comparison between these factors in single and multiple implant failure scenarios in
patients with multiple dental implants would be valuable to identify potential factors
associated with multiple implant failures. The advantages of a retrospective design would
be superior ability to evaluate the occurrence of a rare outcome in a cost-effective
manner. The results of this retrospective study may enable clinicians to minimize the risk
of multiple implant failure by identifying factors with possible association with the
multiple implant failure phenomenon, and generate hypotheses for further investigation in
prospective studies
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3 Aims, Objectives and Hypothesis
3.1 Aims and Objectives
3.1.1 Primary Objective:
• identify and compare possible risk indicators associated with failure of multiple
versus single dental implants
3.1.2 Secondary Objectives:
• to identify risk of implant failure
• to identify the risk of multiple implant failure among patients with multiple
implants
3.2 Hypotheses
• the null hypothesis is that among patients with multiple implants, there is no
difference between the patient- and implant-related factors which are associated
with multiple implant failure and single implant failure.
• the alternative hypothesis is that among patients with multiple implants, there is a
difference between the patient- and implant-related factors which are associated
with multiple implant failure and single implant failure.
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4. Manuscript for Future Publication
A Retrospective Analysis of Multiple Dental Implant Failures
Elahe Behrooz1 DDS MBA, David Chvartszaid 1, 2 DDS MSc MSc FRCD(C), Jim Yuan
Lai1 DDS MSc PhD FRCD(C), Amir Azarpazhooh1-3 DDS MSc PhD FRCD(C)
1. Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
2. Department of Dentistry, Mount Sinai Hospital, Toronto, ON, Canada
3. Institute of Health Policy, Management and Evaluation, University of Toronto,
Toronto, ON, Canada
Corresponding author: Dr. David Chvartszaid, 124 Edward St, Toronto, ON, Canada,
M5G 1G6
-word count: 4577 -total word count (Abstract to Acknowledgments): 4,931 -total number of tables/figures: 4 -number of references: 33 Keywords: cluster phenomenon, risk indicators, implant success, implant survival, multivariate analysis, loss of osseointegration
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ABSTRACT
Purpose: The purpose of this research is to identify and compare possible risk indicators
associated with failure of multiple versus single dental implants
Materials and Methods: This is a retrospective study on patients who have received
more than one dental implant and have experienced failure of one or more implants at the
Faculty of Dentistry, University of Toronto (January 1979 to June 2018). Data was used
to identify possible factors associated with multiple dental implant failures and compare
the factors between individuals with single and multiple implant failures. Associations
between various factors and multiple implant failure were evaluated with univariate and
multivariate logistic regression models.
Results: Failure rates were 5.5% in the total study population with 3.5% in the SIF group
and 7.5% in the MIF group. If history of implant failure was excluded, the following
factors were found to be associated with MIF: machined surfaces (OR: 2.43; 95% CI:
1.47-4, p<0.001), post-operative infections (OR: 2.42; 95% CI: 1.46-4.03, p=0.001), and
the following prostheses opposing the failed implant(s): conventional complete
removable dental prosthesis (OR: 2.54; 95% CI: 1.20-5.36, p=0.015), conventional
removable partial dental prosthesis (OR: 6.50; 95% CI: 1.49-28.39, p=0.013) or full-arch
implant-supported fixed dental prosthesis (OR: 2.33; 95% CI: 1.04-5.24, p=0.040),
periodontitis (controlled (OR: 2.07; 95% CI: 1.19-3.60, p=0.01), uncontrolled (OR: 2.84;
95% CI: 1.33-6.06, p=0.007), and positive history (OR: 4.63; 95% CI: 2.13-10.10,
p<0.001), alcohol consumption (OR: 2.95; 95% CI: 1.05-8.32, p=0.041), history of
chemotherapy (OR: 11.13; 95% CI: 1.4-88.65, p=0.023), and use of antidepressant
medications (OR: 2.95; 95% CI: 1.46-5.97, p=0.003).
Conclusions: Provision of implant-based care for patients presenting with factors
associated with multiple implant failure should be undertaken with caution.
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INTRODUCTION
Titanium dental implants have been utilized to support intraoral prostheses since the late
1960s with high survival and success rates (Branemark et al. 1977; Branemark 1983). In
assessing an implant’s ability to fulfill its intended objective of support and retention to a
fixed or removable dental prosthesis, three clinical outcomes can be distinguished –
implant success, survival and failure. Smith and Zarb (1989) proposed six criteria for
success of dental implants which can be utilized to assess outcomes. An implant that does
not fulfill its intended functions of retention and support is said to have failed. Implant
survival is observed when an implant is present in the mouth and serves its intended
function but does not fulfill all the predefined success criteria.
Multiple systematic reviews have demonstrated the success of dental implants in
restoration of completely and partially edentulous patients (Papaspyridakos et al. 2014;
Kern et al. 2016; Moraschini et al. 2015; Lindh et al. 1998; Creugers et al. 2000; Jung et
al. 2008). Despite high success rates, implant failures do occur. The etiology of implant
failure can be divided into biological implant failure (i.e., failure of the osseointegration
phenomenon) and other types of failure (mechanical, iatrogenic, and patient adaptation)
(Esposito et al. 1998(I, II)). Biological failure of endosseous implants is defined as
inadequacy of the host bone tissue to establish or maintain osseointegration. Much
research has been done on possible risk factors, with the most emphasis being placed on
smoking, diabetes, history of periodontal disease and history of implant failure
(Moraschini et al. 2016; Chrcanovic et al. 2014; Monje et al. 2014; Weyant and Burt
1993; Chrcanovic et al. 2017). Other factors such as occlusal forces, bruxism and surgical
experience have also been mentioned in the literature (Manfredini et al. 2014; Zhou et al.
2016).
Multiple implant failure (cluster failure) is the failure of multiple implants within the
same individual. This phenomenon is a rare but serious complication with a significant
negative impact on the status or prognosis of the prosthodontic rehabilitation and
remaining implants that imposes a significant psychological, biological and financial
burden on the patient (Jemt and Hager 2006). Clinical studies on multiple implant failures
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are limited and vary tremendously in methodology, patient population and definition of
implant failure (Jemt and Hager 2006). Despite existing research, the implant cluster
failure phenomenon remains poorly understood (Weyant and Burt 1993).
Literature suggests that implant losses cluster in high-risk individuals. Jemt and Hager
(2006) reported that 1.3% of patients with an edentulous maxilla lose their implant-
supported full-arch fixed dental prostheses within 3 years of insertion due to multiple
implant failure. Ekfeldt et al. (2001) identified lack of bone support and/or suboptimal
bone quality, heavy smoking habits and bruxism as factors associated with failure of
multiple implants. In another study by Jemt and Hager (2006), bone quantity, smoking
habits and history of periodontitis have been reported to have a significant impact on
increased implant failure risk, and it has been noted that in some patients implant failures
start predominantly in one quadrant, resulting in an unfavorable distribution of the
remaining implants. This indicates that cluster implant failure may be the result of
multiple factors, some of which are responsible for failure of the first few implants (e.g.,
poor bone quality) while others play a role in the failure of remaining implants (e.g.,
loading factors) (Jemt and Hager 2006). Chrcanovic et al. (2017) reported that less than
5% of the patient population accounted for 56% of all implant failures. Their research
suggested that intake of antidepressants and medications to reduce the gastric acid
production, bruxism, machined surface implants, short implants, poor bone quality and
smoking were potential risk factors for dental implant failures.
The literature is unclear if implant failure and multiple implant failure are distinct
phenomena and if risk indicators for implant failure and multiple implant failure are the
same. Therefore, the primary objective of this study was to identify and compare possible
risk indicators associated with failure of multiple versus single dental implants. The
secondary objective was to identify risk of implant failure and risk of multiple implant
failure among patients with multiple implants. The null hypothesis was that among
patients with multiple implants, there is no difference between the patient- and implant-
related factors which are associated with multiple implant failure and single implant
failure.
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METHODS AND MATERIALS
This is a retrospective cohort study, approved by the University of Toronto Research
Ethics Board (protocol #36362). The total study population was the health records (dental
charts and electronic health records) of patients who had received multiple dental
implants at the Faculty of Dentistry, University of Toronto from January 1979 to June
2017. Patients with more than one implant placed and one or more implants failed due to
loss of osseointegration or severe bone loss were included. Patients with only one implant
placed (with or without failures), patients with no failure of dental implants, and patients
with implant failure for reasons other than loss of osseointegration or severe bone loss
(e.g., implant fracture) were excluded. The included patients were grouped as either
having had 2 or more dental implants with a single implant failure (SIF), or having had 2
or more dental implants with multiple implant failures (MIF). The Lekholm and Zarb
classification was utilized to classify implant recipient bone based on quality (1-4) and
quantity (A-D) (Lekholm and Zarb 1985).
Data Collection: Data was collected and recorded in standardized data extraction sheets
regarding the following factors:
• patient-level factors:
o demographics (age, gender)
o maintenance regularity
o medical history and systemic health (e.g., diabetes, osteoporosis,
chemotherapy, radiation therapy in the head and neck, etc.)
o local factors (e.g., uncontrolled periodontitis or history of periodontitis,
history of implant failure, etc.)
o habits/behaviors (e.g., smoking, alcohol consumption, etc.)
o medications used by the patient (bisphosphonates, chemotherapeutics,
corticosteroids, antidepressants, proton-pump inhibitors, SSRIs, NSAIDs)
• implant-level factors:
o details of implant treatment (e.g., year of implant placement and failure,
site[s], number of placed and failed implants, implant characteristics [brand,
sub-brand, diameter, prosthetic platform diameter, length, surface], prosthesis
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type supported by the implant(s), type of prosthesis or natural dentition
opposing the implant(s), follow-up)
o details of implant failure (e.g., site[s], longevity, type of failure [early vs. late],
number of failed implants)
o local factors (e.g., uncontrolled parafunction, bone quality and quantity, bone
augmentation, etc.)
o operator- and surgery-related factors (e.g., surgeon’s experience, occurrence
of intra- and post-operative complications)
Statistical Analysis: Data were analyzed to determine differences in frequencies of
occurrence in the data variables between SIF and MIF groups. A univariate regression
analysis was conducted to compare the effect of each factor in the SIF and MIF groups.
Factors that presented with significance at the univariate level were entered into a
multivariate analysis (cut off p-value of 0.1). Two multivariate analyses were performed
to control for potential confounding effects of variables found to be significantly different
in the univariate analyses. Model A was generated by inclusion of all factors which
presented with significance at the univariate level (including history of implant failure).
Model B was generated by inclusion of all factors which presented with significance at
the univariate level (excluding history of implant failure). Results were presented as an
estimated odds ratio (OR) with a 95% confidence interval (CI) for each prognostic
variable. The odds ratios indicated the strength of association for each patient- and
implant-related factor with the outcome of failure of single or multiple implants. SPSS
software, version 23 was used for statistical analysis of the data (SPSS Inc., Chicago, IL).
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RESULTS
The study population consisted of 321 patients who had more than one implant placed
and had experienced failure of one or more implants. During the study duration, there
were an overall of 572 failed implants out of a total of 10,400 implants placed. The SIF
group consisted of 192 patients (192 failed implants out of a total of 5,485 implants
placed). The MIF group consisted of 129 patients (380 failed implants out of a total of
4,915 implants placed). Failure rates were 5.5% in the total study population, 3.5% in the
SIF group and 7.5% in the MIF group. The above failure rates reflect the proportion of
failure of implants within each group.
Patient-level factors: Patient-level factors are summarized in Table 6. A total of 183
female patients (57%) and 138 male patients (43%) were included. The patient average
age at presentation was 55 years for SIM and 55.7 years for MIF groups. The range of
follow-up was from less than 1 year to 36 years after implant placement. Most patients
(n=179 implants, 31.3%) were followed up for 5-9 years, 26.3% of patients (n=150
implants) were followed up for 10-14 years, and 23.8% of patients (n=136 implants) were
followed up for 0-4 years. The remainder of the population (n=107 implants, 18.6%) was
followed up for 15-36 years. With respect to the maintenance regularity, 203 patients
(63.2%) had regular maintenance, 83 patients (25.9%) had irregular maintenance and 35
patients (10.9%) had no maintenance at all.
A total of 193 implants (60.1%) failed in patients with no periodontitis, 64 implants
(19.9%) failed in patients who had current and controlled periodontitis, 31 implants
(9.7%) failed in patients with uncontrolled periodontitis and 33 implants (10.3%) failed in
patients with positive history of periodontitis. In regards to the history of implant failure,
218 implants (67.9%) failed in patients with no history of implant failure, 10 implants
from the SIF group (5.2%) failed in patients with a positive history of implant failure at
the same site, 3 implants from the MIF group (2.3%) failed in patients with a positive
history of implant failure at the same site and elsewhere in the mouth, and 90 implants
from the MIF group (69.8%) failed in patients with a positive history of implant failure
elsewhere in the mouth. With respect to smoking, 245 implants (76.3%) failed in non-
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smokers, 37 implants (11.5%) failed in smokers and 39 implants (12.1%) failed in
patients with a history of smoking. With respect to alcohol consumption, 302 implants
(94.1%) failed in patients with no alcohol consumption, 4 implants (1.2%) failed in
patients who reported social alcohol consumption and 15 implants (4.7%) failed in
patients with extensive alcohol consumption. A total of 295 implants (91.9%) failed in
patients with no diabetes, 4 implants (1.2%) failed in patients who had diabetes controlled
with diet and 22 implants (6.9%) failed in patients who had diabetes controlled with
medication(s). With respect to osteoporosis, 278 implants (86.6%) failed in patients with
no osteoporosis, 33 implants (10.3%) failed in patients who had osteoporosis managed
with medication(s) and 10 implants (3.1%) failed in patients who had osteoporosis but
were on no medication. A total of 315 implants (98.1%) failed in patients with no
chemotherapy, and 6 implants (1.9%) failed in patients who had a positive history of
chemotherapy. In regards to radiation therapy in the head and neck region, 315 implants
(98.1%) failed in patients with no radiation therapy, 1 implant (0.3%) failed in a patient
who was undergoing radiation therapy to the head and neck region, and 5 implants
(1.6%) failed in patients who had a positive history for the radiation to the head and neck.
With respect to the use of medications at the time of implant placement, 10% of patients
were on antidepressants, 10% on antiresorptive medications, 14% used NSAIDs, 6% used
proton-pump inhibitors, 2% used corticosteroids and 7.5% used SSRIs. A total of 286
implants (89.1%) failed in patients who were not on antidepressants and 35 implants
(10.9%) failed in patients who were on antidepressant medication(s), 288 implants
(89.7%) failed in patients who were not on antiresorptive medications and 33 implants
(10.3%) failed in patients who were on antiresorptive medication(s), 274 implants
(85.4%) failed in patients who were not taking NSAIDs and 47 implants (14.6%) failed in
patients who were on NSAIDs, 302 implants (94.1%) failed in patients who were not on
proton-pump inhibitors and 19 implants (5.9%) failed in patients who were using proton-
pump inhibitor medications, 314 implants (97.8%) failed in patients who were not on
corticosteroids and 7 implants (2.2%) failed in patients who were on corticosteroids, and
299 implants (92.2%) failed in patients who were not on SSRIs while 25 implants (7.8%)
failed in patients who were on SSRIs.
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Implant-level factors: Implant-level factors are summarized in Table 7. Overall,
implants were placed from 1979 to 2018, and failures were reported from 1980 to 2018.
Each patient received between 2 to 15 implants, of which 7% to 100% of the implants
failed. In the SIF group, each patient received 2 to 14 implants, with 7% to 50% failure
rate. In the MIF group, each patient received 2 to 15 implants, of which 11% to 100% of
the implants failed.
Most of the failed implants (50.6%) were Branemark (external hex), followed by Nobel
Biocare (20.8%), Zimmer Biomet (11%), Straumann (6.8%), Astra Tech (5.8%) and
Endopore (3.3%). Implant diameters ranged from 3.25 to 5.5 mm, prosthetic platforms
ranged from 3 to 5 mm, and implant lengths ranged from 5 to 16 mm. A total of 302
implants (52.4%) had a machined surface, 251 (44%) had a moderately rough surface,
and 19 (3.6%) had a rough surface. In terms of location of the failed implants, most
failures occurred in the posterior maxilla (30.8%), followed by the anterior maxilla (27%)
and posterior mandible (25.2%), with fewest failures reported in the anterior mandible
(17%). In the SIF group most failures occurred in the posterior mandible, followed by the
posterior maxilla and the anterior maxilla, with fewest failures reported in anterior
mandible. In the MIF group, most failures occurred in the posterior maxilla, followed by
the anterior maxilla and posterior mandible, with fewest failures reported in the anterior
mandible.
With respect to the timing of implant failure, 273 implants (47.9%) experienced early
failures (pre-prosthetic loading), and 299 implants (52.1%) had late failures (post-
prosthetic loading). A total of 243 failed implants (42.6%) were not prosthetically loaded,
whereas 328 implants (57.4%) were utilized to support and/or retain a dental prosthesis.
A total of 229 implants (40%) supported a fixed screw-retained prosthesis, 13 implants
(2%) supported a fixed cement-retained prosthesis and 87 implants (15%) supported a
removable prosthesis. Only 4 failed implants (0.5%) were unopposed by any prosthesis or
natural dentition, and the remaining implants were opposed by natural dentition or
prostheses of different designs and extensions [90 implants (15.8%) opposed by a
conventional complete removable dental prosthesis (CRDP), 17 implants (3%) opposed
by a conventional removable partial dental prosthesis (RPDP), 71 implants (12.4%)
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opposed by a full-arch implant-supported fixed dental prosthesis (FA ISFDP), 39
implants (6.8%) opposed by an implant-supported fixed partial dental prosthesis
(ISFPDP), 9 implants (1.6%) opposed by an implant-supported removable dental
prosthesis (ISRDP) and 342 implants (59.9%) opposed natural dentition]. A total of 529
implants (92.5%) failed in patients with no parafunction, 4 implants (0.7%) failed in
patients with parafunction controlled with a removable interocclusal appliance, and 39
implants (6.8%) failed in patients who had uncontrolled loading risk or parafunction.
In terms of bone quality, for a total of 211 failed implants (36.8%) bone quality was not
specified, a total of 8 failed implants (1.7%) were placed in type 1 bone, 76 failed
implants (13.2%) in type 2 bone, 228 failed implants (39.8%) in type 3 bone and 49 failed
implants (8.5%) were placed in type 4 bone. In terms of bone quantity, for a total of 211
failed implants (36.7%) bone quantity was not specified, 26 failed implants (4.6%) were
placed in type A bone, 69 failed implants (12.1%) were in type B bone, 218 failed
implants (38.2%) were in type C bone, 48 failed implants (8.4%) were placed in type D
bone. A total of 398 failed implants (69.4%) were placed in non-augmented bone, 93
failed implants (16.3%) were placed after major augmentation (direct sinus bone graft,
block bone graft, etc.) and 81 failed implants (14.2%) were placed in sites with minor
augmentation (indirect sinus bone graft, particulate bone graft, etc.).
Intra-operative complications were not reported for 491 of the failed implants (85.3%). A
total of 81 failed implants (14.2%) were associated with intra-operative complications
such as poor primary stability, significant thread exposure, apical fenestration, or a
combination of these complications. Post-operative complications such as infection were
not reported for 391 failed implants (68.3%), while 181 failed implants (31.7%) were
reported to have had post-operative infections. In regards to the surgeon’s experience,
500 failed implants (87.6%) were placed by residents, and 72 failed implants (12.4%)
were placed by specialists.
Univariate regression results: The univariate regression analyses are summarized in
Tables 6 and 7.
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At the patient-level analysis, no statistically significant difference was noted between the
SIF and MIF groups in regards to the patient gender, maintenance regularity, smoking,
diabetes mellitus, osteoporosis, radiation therapy to the head and neck, the use of
antiresorptive medications, NSAIDs, proton-pump inhibitors, corticosteroids and SSRIs.
However, the following factors were found to be associated with MIF: history of
periodontitis (OR: 5.10; 95% CI: 2.29-11.38, p<0.001), history of implant failure (OR:
47.02; 95% CI: 22.35-98.92, p<0.001), alcohol consumption (OR: 3.47; 95% CI: 1.29-
9.39, p=0.014) and history of chemotherapy (OR: 7.70; 95% CI: 0.89-66.71, p=0.04)
(Table 6).
At the implant-level analysis, no statistically significant difference was noted between the
SIF and MIF groups with respect to the implant diameter, prosthetic platform size,
implant length, loading risks and minor augmentation procedures. Moreover, no
statistically significant difference was identified between the SIF and MIF groups with
regards to the 3 implant brands: Nobel Biocare, Straumann and Endopore. However, a
statistically significant higher failure rate in the MIF group was noted with implant
brands Branemark (external hex) (OR: 4.96; 95% CI: 2.79-8.83, p=0.000), Zimmer
Biomet (OR: 0.57; 95% CI: 0.5-1.7, p=0.034) and Astra Tech (OR: 4; 95% CI: 1.62-9.86,
p=0.003). The following factors were found to be associated with MIF: machined
surfaces (OR: 2.05; 95% CI: 1.29-3.27, p=0.002), late failures (OR: 1.46; 95% CI: 1.03-
2.06, p=0.041), major augmentation procedures (OR: 1.83; 95% CI: 1.08-3.11, p=0.024),
occurrence of significant thread exposure/apical fenestration intra-operatively (OR: 1.86;
95% CI: 1.04-3.34, p=0.038), occurrence of infections post-operatively (OR: 1.98; 95%
CI: 1.33-2.96, p=0.001) as well as presence of CRDP (OR: 1.93; 95% CI: 1.15-3.23,
p=0.012) and FA ISFDP (OR: 5.52; 95% CI: 2.57-11.89, p≤ 0.001) opposing the failed
implant. No statistically significant differences were found between SIF and MIF groups
in terms of bone quantity; however, type C bone was significantly more prevalent in the
MIF group (OR: 5.30; 95% CI: 2.15-13.04, p<0.001). Finally, a statistically significant
difference was noted in regards to the operator’s experience with more implants in the
MIF group belonging to the specialist group (OR: 1.87; 95% CI: 1.04-3.36, p=0.043)
(Table 7).
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Multivariate regression results:
Two multivariate analyses were performed to control for potential confounding effects of
variables found to be significantly different in the univariate analyses. Model A was
generated by inclusion of all factors which presented with significance at the univariate
level (including history of implant failure). This model revealed the following factors to
be associated with MIF: machined surfaces (OR: 2.40; 95% CI: 1.23-4.70, p=0.011),
presence of conventional RDPD opposing the failed implant OR: 11.61; 95% CI: 2.49-
54.12, p=0.002), uncontrolled periodontitis (OR: 4.42; 95% CI: 1.65-11.85, p=0.003) and
use of antidepressant medications (OR: 2.49; 95% CI: 1.06-5.81, p=0.035) (Table 8).
Model B was generated similarly to model A but excluded history of implant failure and
revealed the following factors to be associated with MIF: machined surfaces (OR: 2.43;
95% CI: 1.47-4, p<0.001); post-operative infections (OR: 2.42; 95% CI: 1.46-4.03,
p=0.001); presence of conventional CRDP (OR: 2.54; 95% CI: 1.20-5.36, p=0.015),
conventional RPDP (OR: 6.50; 95% CI: 1.49-28.39, p=0.013) or FA ISFDP (OR: 3.20;
95% CI: 1.3-7.85, p=0.011) opposing the failed implant(s); periodontitis [controlled (OR:
2.07; 95% CI: 1.19-3.60, p=0.01), uncontrolled (OR: 2.84; 95% CI: 1.33-6.06, p=0.007)
and positive history (OR: 4.63; 95% CI: 2.13-10.10, p<0.001)]; alcohol consumption
(OR: 2.95; 95% CI: 1.05-8.32, p=0.041); history of chemotherapy (OR: 11.13; 95% CI:
1.4-88.65, p=0.023); and use of antidepressant medications (OR: 2.95; 95% CI: 1.46-
5.97, p=0.003) (Table 9).
DISCUSSION
This study aimed to evaluate factors associated with failure of multiple implants in
comparison to failure of single implants in patients with multiple dental implants. The
results suggest that previous history of implant failure, machined surface implants, post-
operative infections, presence of certain prostheses opposing the failed implant(s)
(conventional CRDP, conventional RPDP, and FA ISFDP), periodontitis, alcohol
consumption, history of chemotherapy and use of antidepressant medications may be
associated with multiple implant failures in comparison to failure of single implants.
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In this study, machined surfaces were associated with failure of multiple implants in
comparison with failure of single implants. Branemark implants with machined surfaces
were used almost exclusively at the University of Toronto in the first 15 years of the
study and primarily for full-arch rehabilitations. By contrast, the other implant brands
with moderately rough surfaces were utilized for partially and completely edentulous
scenarios and have been used for a much shorter period of time. The study inclusion
criteria limited recruitment to patients with multiple implants; hence, Branemark implants
with machined surfaces are disproportionately represented in the study. Lastly, implant
failure is a time-dependent phenomenon, and this further increases the chances of
observing more failures with older brands with a longer duration of follow-up. These
factors need to be considered when interpreting the association of machined Branemark
implants with multiple implant failure in the current research. Implant surfaces have
undergone significant improvement since the evolution of implant dentistry. Machined
surfaces have been modified to moderately rough in attempts to optimize and speed-up
the osseointegration process (Albrektsson and Wennerberg 2019). The Sa values (average
roughness over the surface) for current implant surfaces range from 0.86-1.78
micrometers, indicating a moderately rough surface texture, whereas the Sa value for
machined surfaces was about 0.40 micrometers (Wennerberg and Albrektsson 2009).
Overall, higher survival rates for moderately rough surfaces have been reported in the
literature (Wennerberg et al. 2018).
Many studies have demonstrated higher implant failure rates in the maxilla (e.g., Adell et
al. 1990; Jemt 1991), and the literature on multiple implant failures seems to show a
similar pattern (Jemt and Hager 2006). However, this research does not appear to
demonstrate a difference in association of arch location between failure of multiple and
single implants. Suboptimal bone quality and quantity have also been reported to be risk
factors associated with failure of implants (Becker et al. 1990; Zarb and Schmitt 1989,
1990(I, II, III); Friberg et al. 1991) and cluster implant failure (Ekfeldt et al. 2001; Jemt
and Hager 2006; Chrcanovic et al. 2017). This study did not demonstrate a statistically
significant difference between single and multiple implant failures with respect to bone
quality and quantity. The lack of the association of arch location with multiple implant
failure in comparison to single implant failure may be related to the lack of association of
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bone quality and quantity with multiple implant failure in comparison to single implant
failure.
Parafunction has been strongly associated with mechanical complications (Zhou et al.
2016); yet, the effect of parafunction on implant failure is controversial (Manfredini et al.
2014). Some authors have suggested that parafunction contributes to implant failure
(Zhou et al. 2016) and multiple implant failure (Ekfeldt et al. 2001; Chrcanovic et al.
2017). The results of this study did not show a difference between failure of single and
multiple implants with respect to parafunction.
The current results suggest that presence of certain prostheses opposing the implant(s)
(conventional CRDP, conventional RPDP or FA ISFDP) may be associated with multiple
implant failure. These finding are difficult to interpret. One possible explanation may be
that patients who have experienced loss of a large number of teeth may have presented
with factors which contributed to both the loss of many teeth and the loss of multiple
implants.
Numerous factors have been identified in the literature to be associated with failure of
multiple dental implants. These factors include smoking (Chrcanovic et al. 2017),
diabetes (Ekfeldt et al. 2001), history of periodontitis (Jemt and Hager 2006), occlusal
forces and bruxism (Ekfeldt et al. 2001; Chrcanovic et al. 2017), use of proton-pump
inhibitors (Chrcanovic et al. 2017), use of antidepressants (Chrcanovic et al. 2017),
suboptimal bone quality and quantity (Ekfeldt et al. 2001; Jemt and Hager 2006;
Chrcanovic et al. 2017), location of implant placement (Jemt and Hager 2006) and
implant surface (machined) (Chrcanovic et al. 2017). This study compared the role of
factors in association with failure of single and multiple implants in patients with multiple
dental implants and did not demonstrate a difference between the two groups regarding
smoking, diabetes, occlusal forces and bruxism, the use of proton-pump inhibitors, bone
quality and quantity and location of implant placement. However, machined surfaces,
periodontitis and use of antidepressant medications were associated with clustering of
implant failures in comparison with failure of single implants. The history of post-
operative infection, certain prostheses opposing the failed implant(s), and history of
chemotherapy have not been assessed in the context of failure of multiple implants prior
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to the current study.
The use of SSRIs, NSAIDs and proton-pump inhibitors have been reported to be
associated with an increased risk of implant failure (Wu et al. 2014; Winnett et al. 2016;
Chrcanovic et al. 2017). Furthermore, Chrcanovic et al. (2017) reported intake of
antidepressants, medications to reduce gastric acid production and antithrombotic
medications as risks for multiple implant failure (Chrcanovic et al. 2017). By contrast,
use of corticosteroids and antihypertensive medications have been reported to be
associated with a decrease in implant failure risk (Carr et al. 2003; Wu et al. 2016). This
study demonstrated a statistically significant association between use of antidepressant
medications and failure of multiple implants in comparison to failure of single implants.
However, no association was found between use of antiresorptives, NSAIDs, proton-
pump inhibitors, corticosteroids, SSRIs and failure of multiple implants in comparison to
failure of single implants.
The current study identified history of implant failure to be strongly associated with
multiple implant failure. This finding is in agreement with existing research. Schwartz-
Arad et al. (2008) reported implant survival rates of 77% in individuals with previously
failed implants. A systematic review by Quaranta et al. (2014) reported survival rates of
71-100% for implants placed in areas of previously failed implants and a survival rate of
83.7% for implants replaced for the second time (third attempts) after failure of previous
implants. Similarly, Chrcanovic et al. (2017) reported lower survival rates for implants
replacing failed ones (73%) in comparison with implants placed for the first time (94%).
This study employed a retrospective design as it is most suited to assess the burden of
illness and to analyze risk factors when the outcome is rare (Fletcher and Fletcher 2005).
The advantages of this study over the previous studies on multiple implant failure are a
longer follow-up time, large sample size, and presence of a comparison group allowing
for a comparative analysis, all of which add to the rigor of the methodology. However,
this study has shortcomings due to the retrospective design such as limitations of existing
data set and relying on accuracy of chart entries. Moreover, due to significant
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improvement in implant surfaces and clinical techniques, some results may not be fully
generalizable to the contemporary clinical practice.
Future research should focus on confirmation of risk factors for multiple implant failure,
identification of high-risk individuals, as well as creation of recommendations and
clinical practice guidelines for management of patients with a history of multiple implant
failure.
CONCLUSION
Within the limitations of this study, several factors were identified to be associated with
failure of multiple implants. In the presence of multiple risk indicators in high-risk
individuals, it may be prudent to consider alternative prosthodontic treatments.
ACKNOWLEDGMENTS
We acknowledge the contribution of the implant database team of the Implant
Prosthodontic Unit at the Faculty of Dentistry, University of Toronto – Mrs. Janet
deWinter and Mr. Hanif Malek – for their assistance in gaining access to the charts. We
are also grateful to Mr. Anton Svendrovski for the statistical analysis of the data.
Conflicts of interest: the authors declare no conflicts of interest.
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Table 6: Patient-level risk indicator analysis using a univariate estimating equations logistic regression model
Patient-level factors
Overall (n=321)
MIF group (n=129)
SIF group (n=192)
OR (95% CI) p-value for significance
Patient age at presentation (years) Mean ± SD (range), median
55 ± 11.97 (12-86) Med: 56
55.7 ± 11.6 (24-84) Med: 57
55 ± 12.24 (12-86) Med: 55
1.01 (0.99-1.02)
0.59
Patient gender
Female (Ref) Male
183 (57%) 138 (43%)
70 (57.2%) 59 (42.8%)
113 (58.9%) 79 (41.1%)
1.21 (0.77-1.89)
0.42
Maintenance regularity
Regular maintenance (at least every 2-3 years)
203 (63.2%) 76 (58.9%) 127 (66.1%) 0.80 (0.39-1.65)
0.54
Irregular maintenance
83 (25.9%) 38 (29.5%) 45 (23.4%) 1.13 (0.51-2.50)
0.77
No maintenance (Ref)
35 (10.9%) 15 (11.6%) 20 (10.4%) N/A
General risk indicators
History of periodontitis
No (Ref) 193 (60.1%) 60 (46.5%) 133 (69.3) N/A
Yes (current, controlled)
64 (19.9%) 26 (20.2%) 38 (19.8%) 1.52 (0.85-2.72)
0.163
Yes (current, uncontrolled)
31 (9.7%) 20 (15.5%) 11 (5.7%) 4.03 (1.82-8.94)
0.001^
Yes, history* 33 (10.3%) 23 (17.8%) 10 (5.2%) 5.10 (2.29-11.38)
<0.001^
this site 1 (3%) 1 (4%) 0 (0%)
this site + elsewhere in the mouth
27 (82%) 20 (87%) 7 (70%)
elsewhere in the mouth
5 (15%) 2 (9%) 3 (30%)
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Patient-level factors
Overall (n=321)
MIF group (n=129)
SIF group (n=192)
OR (95% CI) p-value for significance
History of implant failure
No 218 (67.9%) 36 (27.9%) 182 (94.7%)
47.02 (22.35-98.92)
<0.001^
Yes** 103 (32.1%) 93 (72.1%) 10 (5.3%)
this site 10 (3.1%) 0 (0%) 10 (5.3%)
this site + elsewhere in the mouth
3 (0.9%) 3 (2.3%) 0 (0%)
elsewhere in the mouth
90 (28%) 90 (69.8%) 0 (0%)
Smoking
Yes, current 37 (11.5%) 16 (12.4%) 21 (10.9%) 1.25 (0.62-2.51)
0.54
Positive history 39 (12.1%) 20 (15.5%) 19 (9.9%) 1.72 (0.87-3.39)
0.117
No (Ref) 245 (76.3%) 93 (72.1%) 152 (79.2%) N/A
Alcohol consumption***
Yes, extensive 15 (4.7%) 10 (7.8%) 5 (2.6%)
3.47 (1.29-9.39)
0.014^
Yes, social 4 (1.2%) 3 (2.3%) 1 (0.6%)
No (Ref) 302 (94.1%) 116 (89.9%) 186 (96.8%)
Diabetes
Yes
Yes, controlled with medication(s)
22 (6.9%) 13 (10.1%) 9 (4.7%) 2.33 (0.96-5.62)
0.061
Yes, controlled with diet
4 (1.2%) 3 (2.3%) 1 (0.5%) 4.83 (0.50-47.02)
0.175
No (Ref) 295 (91.9%) 113 (87.5%) 182 (94.7%) N/A
Osteoporosis
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Patient-level factors
Overall (n=321)
MIF group (n=129)
SIF group (n=192)
OR (95% CI) p-value for significance
Yes**** 43 (13.4%) 20 (15.5%) 23 (12%) 1.35 (0.71-2.57)
0.405
Yes, managed with medication(s)
33 (10.33%) 16 (12.4%) 17 (8.3%)
Yes, no medication(s)
10 (3.1%) 4 (3.1%) 6 (3.7%)
No (Ref) 278 (86.6%) 109 (84.5%) 169 (88%)
Chemotherapy
Positive history 6 (1.9%) 5 (3.9%) 1 (0.5%) 7.70 (0.89-66.71)
0.04^ No 315 (98.1%) 124 (96.1%) 191 (99.5%)
Radiation therapy in the head and neck
Yes, current*****
1 (0.3%) 0 (0%) 1 (0.5%)
1.50 (0.30-7.55)
0.688 Positive history 5 (1.6%) 3 (2.3%) 2 (1%)
No 315 (98.1%) 126 (97.7%) 189 (98.4%)
Medication-related risk indicators
Use of antidepressant medication(s)
Yes 35 (10.9%) 20 (15.5%) 15 (5.2%) 2.17 (1.06-4.41)
0.043^ No 286 (89.1%) 109 (84.5% 177 (94.8%)
Use of antiresorptive medication(s) (Bisphosphonates and RANK ligand inhibitors)
Yes 33 (10.3%) 16 (12.4%) 17 (8.8%) 1.46 (0.71-3.00)
0.35 No 288 (89.7%) 113 (87.6%) 175 (91.2%)
Use of Non-steroidal anti-inflammatory drugs (NSAIDs)
Yes 47 (14.6%) 22 (17.1%) 25 (13.1%) 1.37 (0.74-2.56)
0.337 No 274 (85.4%) 107 (82.9%) 167 (86.9%)
Use of proton-pump inhibitors
Yes 19 (5.9%) 11 (8.6%) 8 (4.2%) 2.14 (0.84-5.49)
0.146 No 302 (94.1%) 118 (91.4%) 184 (95.8%)
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Patient-level factors
Overall (n=321)
MIF group (n=129)
SIF group (n=192)
OR (95% CI) p-value for significance
Use of corticosteroids
Yes 7 (2.2%) 3 (2.4%) 4 (2.1%) 1.12 (0.25-5.09)
1.000 No 314 (97.8%) 126 (97.6%) 188 (97.9%)
Use of SSRIs
Yes 25 (7.8%) 15 (11.7%) 10 (5.3%) 2.40 (1.04-5.51)
0.054 No 296 (92.2%) 114 (88.3%) 182 (94.7%) -abbreviations: SD (standard deviation), Med (median), Ref (reference category) -reference subcategories of variables are indicated as Ref -p-values depicted with ^ indicate a statistically significant association between the variable and the failure of multiple dental implants * merged histories for periodontitis for the statistical analysis ** merged the items in the yes category for the statistical analysis *** merged extensive and social alcohol consumption for statistical analysis **** merged items in the yes category for statistical analysis ***** eliminated the single control for statistical analysis
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Table 7: Implant-level risk indicator analysis using a univariate estimating equations logistic regression model
Implant-level factors
Overall (n = 321)
MIF group (n = 129)
SIF group (n = 192)
OR (95% CI) p-value for significance
Year of implant placement Mean ± SD (range), median
2003 ± 8.8 (1979-2018) Med: 2006
2003 ± 8.8 (1980-2018) Med: 2004
2005 ± 8.7 (1979-2018) Med: 2007
N/A
Year of implant failure Mean ± SD (range), median
2007 ± 9.3 (1980-2018) Med: 2009
2006 ± 9.5 (1980-2018) Med: 2008
2007 ± 8.9 (1980-2018) Med: 2009
N/A
Implant characteristics
Implant diameter, prosthetic platform size and length
Implant diameter 4 ± 0.44 (3.25-5.5) Med: 3.7
3.94 ± 0.41 (3.25-5.5) Med: 3.75
4 ± 0.49 (3.25-5) Med: 3.8
0.72 (0.49-1.06) 0.10
Implant prosthetic platform
4 ± 0.43 (3-5.5)
Med: 3.7
3.9 ± 0.41 (3.25-5.5) Med: 3.7
4 ± 0.44 (3-5)
Med: 3.7
0.97 (0.65-1.45) 0.88
Implant length 10.89 ±1.94 (5-16)
Med: 10
10.95 ± 1.91 (6-16)
Med: 10
10.77 ± 1.99 (5-16)
Med: 10
1.05 (0.96-1.15) 0.31
Implant surface
Machined 302 (52.4%) 221 (58%) 81 (41.7%) 2.05 (1.29-3.27) 0.002^
Moderately rough (Ref)
251 (44%) 150 (39.6%) 101 (53.1%) N/A
Rough 19 (3.6%) 9 (2.4%) 10 (5.2%) 0.84 (0.25-2.82) 0.78
Location of failed implant
Anterior Mx 155 (27%) 118 (30.9%) 37 (19.3%) 1.63 (0.93-2.85) 0.087
Anterior Md (Ref) 97 (17%) 64 (16.9%) 33 (17.2%) N/A
Posterior Mx 176 (30.8%) 121 (31.9%) 55 (28.6%) 1.13 (0.67-1.92) 0.64
Posterior Md 144 (25.2%) 77 (20.3%) 67 (34.9%) 0.59 (0.35-1.01) 0.054
Type of failure
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Implant-level factors
Overall (n = 321)
MIF group (n = 129)
SIF group (n = 192)
OR (95% CI) p-value for significance
Early failure (pre-prosthetic loading) (Ref)
273 (47.9%) 169 (44.9%) 104 (54.2%)
1.46 (1.03-2.06) 0.041^
Late failure (post-prosthetic loading)
299 (52.1%) 211 (55.1%) 88 (45.8%)
Loading risk due to parafunction*
No (Ref) 529 (92.5%) 354 (93.1%) 175 (91.1%)
1.01 (0.51-2.02) 1.000
Yes, controlled (removable interocclusal appliance)
4 (0.7%)
0 (0%)
4 (2.1%)
Yes, not controlled 39 (6.8%) 26 (6.9%) 13 (6.8%)
Bone quality and quantity**
Bone quality
1 (Ref) 8 (1.7%) 4 (1%) 3 (1.5%) N/A
2 76 (13.2%) 52 (13.6%) 24 12.5%) 1.30 (0.29-5.89) 0.73
3 228 (39.8%) 200 (35%) 29 (15.1%) 4.14 (0.94-18.24) 0.061
4 49 (8.5%) 36 (6.2%) 13 (6.9%) 1.66 (0.35-7.95) 0.53
Not specified 211 (36.8%) 88 (44.2) 123 (64%) N/A
Bone quantity
A (Ref) 26 (4.6%) 16 (4.2%) 10 (5.2%) N/A
B 69 (12.1%) 48 (12.7%) 21 (10.9%) 1.43 (0.56-3.66) 0.46
C 218 (38.2%) 195 (51.5%) 23 (12%) 5.30 (2.15-13.04) <0.001^
D 48 (8.4%) 33 (8.7%) 15 (7.8%) 1.38 (0.51-3.73) 0.53
Not specified 211 (36.7%) 88 (22.9%) 123 (64.1%) N/A
Bone augmentation
No (Ref) 398 (69.4%) 259 (68.1%) 139 (71.9%) N/A
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Implant-level factors
Overall (n = 321)
MIF group (n = 129)
SIF group (n = 192)
OR (95% CI) p-value for significance
Yes, minor (indirect sinus bone graft + particulate bone graft)
93 (16.3%)
72 (19%)
21 (10.9%)
0.82 (0.50-1.34) 0.43
Yes, major (direct sinus bone graft + block graft)
81 (14.2%) 49 (12.9%) 32 (16.7%) 1.83 (1.08-3.11) 0.024^
Complications
Intra-operative complications
Poor primary stability
9 (1.6%) 5 (1.3%) 4 (2.1%) 0.66 (0.18-2.51) 0.55
Significant thread exposure/apical fenestration
72 (12.6%) 56 (14.8%) 16 (8.3%) 1.86 (1.04-3.34) 0.038^
None reported (Ref)
491 (85.3%) 319 (83.9%) 172 (88%) N/A
Post-operative complications
Infections 181 (31.7%) 138 (76.2%) 43 (22.4%) 1.98 (1.33-2.96) 0.001^
No infections (Ref) 391 (68.3%) 242 (61.8%) 149 (77.6%)
Definitive prosthesis type supported by the failed implant(s)
Fixed ISP (screw-retained)
IS-SC (Ref) 31 (5.4%) 10 (2.6%) 21 (10.9%) N/A
IS-FPDP splinted crowns
95 (16.6%) 59 (15.6%) 36 (18.8%) 3.44 (1.46-8.13) 0.005^
FA-IS-FDP 90 (15.8%) 72 (19%) 18 (9.4%) 8.40 (3.37-20.93) <0.001^
IS-FPDP bridge 12 (2.1%) 8 (2.1%) 4 (2.1%) 4.20 (1.02-17.32) 0.047^
Fixed ISP (cement-retained)***
IS-SC (ref) 6 (1.1%) 4 (1.1%) 2 (1%) 3.00 (0.20-45.24) 0.56
IS-FPDP splinted crowns
5 (0.9%) 4 (1.1%) 1 (0.5%)
IS-FPDP bridge 2 (0.4%) 2 (0.5%) 0 (0%)
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Implant-level factors
Overall (n = 321)
MIF group (n = 129)
SIF group (n = 192)
OR (95% CI) p-value for significance
Removable ISP****
Conventional CRDP
30 (5.3%) 30 (7.9%) 0 (0%) 0.006^
RPDP 4 (0.7%) 4 (1.1%) 0 (0%) *****
IS-OD (individual attachments)
29 (5.1%) 25 (6.6%) 4 (2.1%)
IS-OD (bar) 24 (4.2%) 18 (4.7%) 6 (3.1%)
Type of prosthesis or natural dentition opposing the failed implant(s)
Conventional CRDP
90 (15.8%) 66 (17.4%) 24 (12.5%) 1.93 (1.15-3.23) 0.012^
Conventional RPDP
17 (3%) 14 (3.7%) 3 (1.6%) 3.27 (0.92-11.60) 0.066
FA-IS-FDP 71 (12.4%) 63 (16.6%) 8 (4.2%) 5.52 (2.57-11.89) <0.001^
IS-FPDPs 39 (6.8%) 28 (7.4%) 11 (5.7%) 1.79 (0.86-3.71) 0.12
IS-RDP 9 (1.6%) 5 (1.3%) 4 (2.1%) 0.88 (0.23-3.32) 0.85
Natural dentition (Ref)
342 (59.9%) 201 (53%) 141 (73.4%) N/A
No opposing occlusion
4 (0.5%) 3 (0.5%) 1 (0.5%) 1.40 (0.13-15.62) 0.78
Operator’s experience
Resident 500 (87.6%) 324 (85.5%) 176 (91.7%)
1.87 (1.04-3.36) 0.043^
Specialist (Ref) 72 (12.4%) 56 (14.5%) 16 (8.3%)
-abbreviations: SD (standard deviation), Med (median), Ref (reference category) -reference subcategories of variables are indicated as Ref -p-values depicted with ^ indicate a statistically significant association between the variable and the failure of multiple dental implants * no loading risk and controlled loading risk were merged for the statistical analysis ** according to the Lekholm and Zarb classification, bone quality is categorized into four groups according to the proportion and structure of compact and trabecular bone tissue: type 1 (large homogenous cortical/compact bone); type 2 (thick layer of compact bone surrounding a dense trabecular bone); type 3 (thin cortical layer surrounding a dense trabecular bone); type 4 (thin cortical layer surrounding a core of low-density trabecular bone). The quantity of jawbone is broken down into five groups (A, B, C, D, and E) based on the residual jaw shape following tooth extraction. Type A represents the largest bone volume and type E represents the smallest volume of bone.
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*** the splinted crowns and bridge factors were merged for the statistical analysis because of the presence of a zero in the controls **** conventional CRDP and RPDP factors were merged and IS-OD factors were merged for the statistical analysis ***** unable to provide estimated OR due to presence of zeros
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Table 8: Model A generated by inclusion of all variables which presented with significance at the univariate level (including history of implant failure)
Factors entered into multivariate regression analysis OR (95% CI) p-value for significance
Implant diameter 1.03 (0.50-2.11) 0.934
Location of failed implant (Anterior Md as Ref) 0.401
Anterior Mx 1.03 (0.37-2.84) 0.954
Posterior Md 0.62 (0.24-1.57) 0.313
Posterior Mx 1.19 (0.44-3.21) 0.734
Implant surface (moderately rough surface as Ref) 0.038^
Machined surface 2.40 (1.23-4.70) 0.011^
Rough surface 1.92 (0.41-9.08) 0.410
Late vs. early failure (early failure as Ref) 0.67 (0.37-1.21) 0.183
Bone augmentation 0.335
Yes, minor (indirect sinus bone graft + particulate bone graft)
0.44 (0.15-1.32) 0.143
Yes, major (direct sinus bone graft + block bone graft)
0.71 (0.27-1.88) 0.495
Intra-operative complications 0.315
Poor primary stability 0.64 (0.04-10.67) 0.736
Significant thread exposure/apical fenestration 2.25 (0.75-6.72) 0.147
Post-operative complications (Infections) 0.88 (0.42-1.85) 0.732
Type of prosthesis or natural dentition opposing the failed implant(s) (natural dentition as Ref)
0.089
Conventional CRDP 1.90 (0.76-4.78) 0.172
Conventional RPDP 11.61 (2.49-54.12) 0.002^
FA-IS-FDP 1.52 (0.45-5.10) 0.496
IS-FPDPs 1.85 (0.59-5.83) 0.29
IS-RDP 0.78 (0.09-6.91) 0.82
No opposing occlusion 0.96 (0.02-40.91) 0.984
History of periodontitis 0.008^
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Yes (current, controlled) 2.49 (1.17-5.30) 0.018
Yes (current, uncontrolled) 4.42 (1.65-11.85) 0.003^
Yes, history 1.14 (0.37-3.48) 0.820
Alcohol consumption 1.82 (0.51-6.55) 0.360
History of chemotherapy 5.59 (0.48-64.87) 0.169
Use of antidepressant medication(s) 2.49 (1.06-5.81) 0.035^
History of implant failure 90.36 (38.52-211.95) <0.001^
-Ref (reference category) -p-values depicted with ^ indicate a statistically significant association between the variable and failure of multiple dental implants
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Table 9: Model B generated by inclusion of all variables which presented with significance at the univariate level (excluding history of implant failure)
Factors entered into multivariate regression analysis OR (95% CI) p-value for significance
Implant diameter 0.94 (0.56-1.60) 0.82
Location of failed implant (Anterior Md as Ref) 0.002^
Anterior Mx 1.87 (0.84-4.14) 0.123
Posterior Md 0.73 (0.34-1.54) 0.40
Posterior Mx 2.13 (0.99-4.59) 0.054
Implant surface (moderately rough as Ref) 0.002^
Machined surface 2.43 (1.47-4) <0.001^
Rough surface 1.8 (0.59-5.48) 0.297
Late vs. early failure (early failure as Ref) 1.03 (0.67-1.57) 0.9
Bone augmentation 0.09
Yes, minor (indirect sinus bone graft + particulate bone graft)
0.66 (0.33-1.36) 0.262
Yes, major (direct sinus bone graft + block graft) 1.67 (0.83-3.34) 0.149
Intra-operative complications 0.668
Poor primary stability 0.66 (0.13-3.28) 0.607
Significant thread exposure/apical fenestration 1.33 (0.59-2.97) 0.494
Post-operative complications (Infections) 2.42 (1.46-4.03) 0.001^
Type of prosthesis or natural dentition opposing the failed implant (natural dentition as Ref)
0.007^
Conventional CRDP 2.54 (1.20-5.36) 0.015
Conventional RPDP 6.50 (1.49-28.39) 0.013
FA-IS-FDP 3.20 (1.3-7.85) 0.011
IS-FPDPs 2.33 (1.04-5.24) 0.40
IS-RDP 0.83 (0.19-3.62) 0.804
No opposing occlusion 2.19 (0.17-28.22) 0.448
Periodontitis <0.001^
Yes (current, controlled) 2.07 (1.19-3.60) 0.01^
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Yes (current, uncontrolled) 2.84 (1.33-6.06) 0.007^
Yes, history 4.63 (2.13-10.10) <0.001^
Alcohol consumption 2.95 (1.05-8.32) 0.041^
History of chemotherapy 11.13 (1.4-88.65) 0.023^
Use of Antidepressant medication(s) 2.95 (1.46-5.97) 0.003^
-Ref (reference category) -p-values depicted with ^ indicate a statistically significant association between the variable and failure of multiple dental implants
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5 Discussion
5.1 Comparison of Results to Existing Literature
This study aimed to evaluate the risk indicators associated with failure of multiple
implants in comparison to failure of single implants in patients with multiple dental
implants. The results of the current study suggested that in patients with multiple dental
implants, machined surfaces, post-operative infections, presence of certain prostheses
opposing the failed implant(s) (conventional CRDP, conventional RPDP or FA ISFDP),
periodontitis, alcohol consumption, history of chemotherapy, and use of antidepressant
medications were associated with failure of multiple implants in comparison with failure
of single implants.
5.1.1 Multiple Implant Failure Studies
The results of the current study provide some contrast to the findings of the existing
limited research on multiple implant failure. Ekfeldt et al. (2001) conducted a
retrospective study on 54 patients with complete edentulism in the maxillae who had
received either a fixed prosthesis or an overdenture supported by at least 4 implants to
determine the factors associated with multiple implant failures in the maxilla. They found
that factors associated with failure of multiple implants (defined in this study as loss of at
least half of the placed implants) were quality and quantity of bone, bruxism, bone
overheating, diabetes, osteoporosis, personal grief, depression, loading problems as well
as addiction to cigarette smoking, alcohol or narcotics. The authors suggested that
important factors contributing to a cluster phenomenon of implant failures may be lack of
bone support, heavy smoking and bruxism. This study did not demonstrate a difference
between single and multiple implant failures in association with the above factors.
Weyant and Burt (1993) examined multiple implant failure in a retrospective review on
598 patients with 2,098 implants over a 6-year time frame and reported that the odds of
having a second implant removed were reported to be 1.3 times greater if the patient
already had one implant removed. This result agrees with the current study’s finding that
history of implant failure is a very strong predictor for future failures.
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Jemt and Hager (2006) conducted a retrospective study of 1,267 consecutively treated
patients aiming to identify patients who had lost their definitive fixed implant-supported
prosthesis in the maxilla within the first 3 years of prosthesis insertion due to loss of the
supporting implants. They found that bone quantity, smoking and periodontitis have
significant impacts on increased implant failure risk. The results of the present study also
found periodontitis to be associated with failure of multiple implants. However, smoking
and bone quantity did not present with a statistically significant difference between the
single and multiple implant failure groups. The two studies are different in that Jemt and
Hager (2006) evaluated cluster implant failures in the edentulous maxilla over a 3-year
period, whereas this study compared single and multiple implant failures in partially and
completely edentulous patients in both jaws over a much longer time period.
Furthermore, different implant systems were assessed in the two studies. The current
study contained a notable proportion of machined surface Branemark implants, and this is
significant because smoking and bone quantity seem to be more powerful risk factors
with machined surface implants (Balshe et al. 2008).
Chrcanovic et al. (2017) conducted a retrospective cohort study in Sweden on 1,406
patients with at least 3 implants. Among a total of 8,337 implants, 592 failures were
reported. Sixty-seven patients (4.77%) experienced cluster failures accounting for 56.8%
of all failures. Their research identified intake of antidepressants and medications to
reduce gastric acid production, bruxism, machined surface implants, short implants, poor
bone quality and smoking as potential risk factors for dental implant failures. Statistically
significant odds ratio for implant failure at the patient-level was found with intake of
antidepressants, intake of medications to reduce gastric acid production, intake of
antithrombotic medications, smoking and bruxism. This study did not demonstrate a
difference between single and multiple implant failures in association with bruxism,
implant length, bone quality, smoking and use of proton-pump inhibitors. However,
machined surface implants and use of antidepressants were significantly associated with
multiple implant failures in this study above and beyond the association with single
implant failures. Biological and technical failures were not separated in the study by
Chrcanovic et al. (2017), and only a small number of patients were on proton-pump
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inhibitors in the current study’s population. These differences in study designs and study
populations may explain the difference in results.
5.1.2 Implant Failure Studies
Moraschini et al. (2016) conducted a systematic review and meta-analysis on success of
dental implants in smokers vs. non-smokers and reported a statistically significant
difference in marginal bone loss around implants (SMD 0.49, 95%CI 0.07-0.90; p=0.02)
and implant failure rates (OR: 1.96, 95%CI 1.68-2.30; p<0.00001) favoring non-smokers.
Similar results were found by Bain and Moy (1993). Bain and Moy (1993) examined the
association between failure of dental implants and smoking in a group of 2,194
Branemark machined surface implants placed in 540 patients followed up over 6 years
and observed an overall failure rate of 5.92%. This study identified that a significantly
greater percentage of failures occurred in smokers (11.28%) than in nonsmokers (4.76%)
and hypothesized that smoking is a major risk factor for implant failures due to
compromised PMN function. The authors also reported that failure rates increased with
decreasing implant length, uncontrolled diabetes, blood dyscrasias, osteoporosis,
alcoholism, psychiatric conditions, high levels of head and neck radiotherapy, general
surgical contraindications and implant placement in the posterior maxilla (Bain and Moy
1993). The results of the current study did demonstrate a significant difference between
the single and multiple implant failure groups in terms of the effect of alcohol
consumption on implant failure; however, the effect of smoking was not different
between the two groups. Both Bain and Moy (2005) and Moraschini et al (2016)
evaluated implant failure, whereas the present study compared the failure of single and
multiple implants. Furthermore, this study assessed a significant proportion of moderately
rough surface implants, whereas Bain and Moy (2005) results are based entirely on
machined surface Branemark implants. These differences in study designs and types of
implants may explain the differences in study results.
The use of SSRIs, NSAIDs and proton-pump inhibitors have been reported to be
associated with an increased risk of implant failure (Wu et al. 2014; Winnett et al. 2016;
Chrcanovic et al. 2017). Wu et al. (1993) conducted a retrospective cohort study to assess
the effect of SSRIs on the failure of dental implants. A total of 916 implants were
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evaluated in 490 patients, 51 of whom were on SSRIs (94 implants). After a follow-up
time of up to 67 months, failure rates of 10.6% in SSRI users and 4.6% in nonusers were
observed. The results of this study suggest that an increased failure risk with dental
implants could be expected in SSRI users (Wu et al. 1993). Chappuis et al. (2018) also
reported an increased implant failure rate in patients using SSRIs and proton-pump
inhibitors and encouraged clinicians to be aware of medication-related implant failures
when considering implant-based care. With respect to the effect of proton-pump
inhibitors on failure of dental implants, Chrcanovic et al. (2017) conducted a
retrospective cohort study on 3,559 implants in 999 patients and concluded that intake of
proton-pump inhibitors may be associated with an increase in risk of failure of dental
implants. By contrast, the result of the present study did not demonstrate a difference
between single and multiple implant failures in association with the usage of
antiresorptives, NSAIDs, proton-pump inhibitors, corticosteroids, and SSRIs, and this
may be related to the presence of few users of these medications in the current study. The
only medication class that this study demonstrated an association of with failure of
multiple implants in comparison to failure of single implants was the use of
antidepressant medications.
Two recent systematic reviews (Ata-Ali et al. 2016; Medes et al. 2019) reviewed the
effect of bisphosphonate therapy on dental implant outcomes and concluded that
bisphosphonate users did not present with higher implant failure rates. These conclusions
match the current study’s findings of no difference between single and multiple implant
failure groups with respect to bisphosphonate therapy.
Retrospective cohort study by Alsaadi et al. (2007) on patients receiving implant
treatment at the Catholic University, Leuven between 1982 and 2003 assessed local and
systemic factors associated with implant failures. The authors examined 2004 consecutive
patients treated with 6,946 Brånemark implants and reported a failure rate of 3.6%. Many
of the assessed factors were found not to be associated with an increased incidence of
early failures, and these factors included cardiac and gastric diseases, controlled diabetes
type II, coagulation problems, hypertension, hypo- or hyperthyroidism,
hypercholesterolemia, asthma, radiotherapy of the treatment site, claustrophobia,
antibiotic therapy, antidepressants and corticosteroid medications. By contrast, Crohn’s
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disease, osteoporosis, smoking, implant-related factors (length, diameter and location)
and vicinity with the natural dentition were significantly associated with early implant
failures (Alsaadi et al. 2007). The current study did not find a difference between single
and multiple implant failure groups with respect to any of the factors that were found to
be significantly associated with early implant failure by Alsaadi et al. (2007). It is
important to keep in mind that Alsaadi et al. (2007) assessed only early failures and did
not focus on multiple implant failure. This present study did not examine Crohn’s disease
because it is not a commonly reported factor for implant failure, and Alsaadi et al. (2007)
is one of the only major studies to report on this risk factor.
Chrcanovic et al. (2014) and Morashchini et al. (2016) reported that diabetes mellitus,
particularly if uncontrolled, is associated with an increase in marginal bone loss around
implants, but it does not significantly affect implant failure rates. While this study did not
evaluate marginal bone levels; it observed no statistically significant difference between
single and multiple implant failure groups in terms of the diabetic risk.
The current study showed an association between periodontitis and history of
periodontitis with failure of multiple implants. The association of implant failure and
periodontitis has been reported previously in some studies (e.g., Sousa et al. (2016)),
although there is no universal agreement on this association. Monje et al. (2014) reported
high survival rates for implants placed in patients with severe forms of periodontal
disease. By contrast, Sousa et al. (2016) reported higher rates of implant loss and
biological complications in patients with a history of severe forms of periodontitis. Some
similarities between the pathogenesis of periodontitis and peri-implantitis, the overlap of
their risk factors, and some histopathological differences between them have contributed
to some controversy on the association of history of periodontitis with implant failure
(Heitz-Mayfield and Lang 2010; Berglundh et al. 2011).
This study identified history of implant failure to have a strong association with failure of
multiple implants. History of implant failure has been reported as a predictor for future
failures in other studies. Weyant and Burt (1993) reported that the odds of a second
implant being removed may be 1.3 times greater in patients with a positive history
implant failure (Weyant and Burt 1993). Schwartz-Arad et al. (2008) reported implant
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survival rates of 77% in individuals with previously failed implants. A recent systematic
review by Oh et al. (2019) reported 86.3% survival rates for implants after retreatment
with follow-up times ranging from less than a year to over 5 years. The authors also
reported that survival rates were higher for retreated implants with moderately rough
surfaces in comparison to machined surface implants (90% vs. 68.7%). Similarly, a
systematic review by Quaranta et al. (2014) reported survival rates of 71-100% for
implants placed in areas of previously failed implants and a survival rate of 83.7% for
implants placed for the second time (third attempts) after failure of previous implants.
Exposure to the same endogenous and exogenous factors which possibly led to the initial
failure, may lead to the repeated failure of the implants. Chrcanovic et al. (2017) also
reported statistically significant lower survival rates of 73% for implants placed in the
sites of initially failed implants and suggested a possible effect of local site-specific risk
factors. Its noteworthy that a large OR was calculated regarding the association of history
of implant failure with MIF. This should be interpreted (mathematically) with extreme
caution, as the number would be exaggerated due to the fact that both SIF and MIF
groups included patients with failure of dental implants. The comparison group was
chosen to be patients with failure of single implants (as opposed to patients with no
failures) to allow for comparing factors with possible associations with failure of
implants between patients with single and multiple implant failures. Therefore, this study
could evaluate the association of factors with multiple implant failures beyond which
would be expected in single implant failure situations.
Antalainen et al. (2013) assessed removed dental implants registered in the Finnish
Dental Implant Register from 1994 to 2012 and reported that shorter implants (8 mm or
less) were more frequently removed than longer implants. The authors also reported that
IMZ implants were most frequently removed and Branemark Nobel Direct implants were
least frequently removed with removal rates of 8.5% and 0.6% respectively. The two
most commonly used implant systems in Finland (60.9% of all implants placed),
Straumann and Astra, demonstrated the same removal rate of 1.2%. The current study did
not reveal a significant difference between the SIF and MIF groups regarding implant
length and brand. Numerous differences exist between this study and the one by
Antalainen et al. (2013) including study design (retrospective cohort vs. survey),
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treatment setting (private practice vs. university), geographic location (Finland vs.
Canada), implant brand mix, and cause of implant removal (failure vs. unspecified) and
these differences may account for the difference in results.
Some authors have suggested that parafunction contributes to failure of implants (Zhou et
al. 2016), and that parafunction is a particularly important risk factor for cluster implant
failure (Ekfeldt et al. 2001; Chrcanovic et al. 2017). Zhou et al. (2016) conducted a meta-
analysis and reported bruxism to be a contributing factor to failure of implant-supported
prostheses, technical and biological complications of dental implants, and implant failure.
More recently, De Angelis et al. (2017) evaluated clinical outcomes in 225 patients with
871 implants after a follow-up time of 10-18 years. De Angelis et al. (2017) considered
the following risk factors: smoking, bruxism, bone augmentation procedures and the
presence of a loading risk (implants with a crown/implant ratio >0.8, angulation >25°,
and presence of a cantilever). Among the analyzed factors, bruxism was the most
significant risk factor for implant failure, and this study proposed bruxism as an absolute
contraindication for implant treatment. Although the results of De Angelis et al. (2017)
and Zhou et al. (2016) are intriguing, these studies did not assess failure of multiple
implants, did not compare factors in regards with failure of single and multiple implants,
and did not differentiate between biological and mechanical failures. The results of the
current study did not find a difference between SIF and MIF with respect to bruxism.
This study did not demonstrate a difference between the multiple and singe implant
failure groups with respect to smoking. Balshe et al. (2008) conducted a retrospective
chart review of 593 patients with 2,182 machined surface implants and 905 patients with
2,425 moderately rough surface implants. Smoking was not associated with implant
failure of moderately rough surface implants (HR: 0.8, 95%CI: 0.3-2.1, p=0.68) but was
associated with failure of machined surface implants (HR: 1.3, 95%CI: 1.6-5.9, p<0.001).
Failure of machined surface implants was significantly more prevalent in posterior
maxilla in smoker patients. Smoking was determined as a risk factor for implant failure
only for machined surface implants and the provision of roughness on dental implant
surfaces by anodizing, blasting, acid etching and plasma spraying was recommended to
reduce the effect of smoking on implant failure (Balshe et al. 2008). This study did not
demonstrate a significant difference between single and multiple failures in association
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with smoking. This study included a large number of moderately rough surface implants
which may be less sensitive to the effects of smoking.
Many studies have demonstrated higher implant failure rates in the maxilla (e.g., Adell et
al. 1990; Jemt 1991), and the literature on multiple implant failures seems to show a
similar pattern (Jemt and Hager 2006). However, this research does not appear to
demonstrate a difference between failure of multiple and single implants with respect to
arch location. Suboptimal bone quality and quantity have also been reported to be risk
factors associated with failure of implants (Becker et al. 1990; Zarb and Schmitt 1989,
1990(I, II, III); Friberg et al. 1991) and cluster implant failure (Ekfeldt et al. 2001; Jemt
and Hager 2006; Chrcanovic et al. 2017). The current study did not demonstrate a
statistically significant difference between single and multiple implant failures with
respect to bone quality and quantity. The lack of the association of arch location with
multiple implant failure in comparison to single implant failure may be related to the lack
of association of bone quality and quantity with multiple implant failure in comparison to
single implant failure.
In this study, machined surfaces were associated with failure of multiple implants in
comparison with failure of single implants. Branemark implants with machined surfaces
were used almost exclusively at the Faculty of Dentistry in the first 15 years of the study
and primarily for full-arch rehabilitations. By contrast, the other implant brands with
moderately rough surfaces were utilized for partially and completely edentulous scenarios
and have been used for a much shorter period of time. The study inclusion criteria limited
recruitment to patients with multiple implants; hence, Branemark implants with machined
surfaces are disproportionately represented in the study. Lastly, implant failure is a time-
dependent phenomenon, and this further increases the chances of observing more failures
with older implant brands with a longer duration of follow-up. These factors need to be
considered when interpreting the association of machined surface Branemark implants
with multiple implant failure in the current research.
In general the surface of the implant has been reported to be an important factor in
establishment and maintenance of osseointegration. Implant surfaces have undergone
significant modifications and improvements since the evolution of implant dentistry.
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Machined and smooth surfaces have been enhanced to moderately rough in attempts to
optimize and speed-up the osseointegration process (Albrektsson and Wennerberg 2019).
The Sa values (average roughness over the surface) for current implant surfaces range
from 0.86-1.78 micrometers indicating a moderately rough surface texture, whereas the
Sa value for machined surfaces was about 0.40 micrometers (Wennerberg and
Albrektsson 2009). Overall higher survival rates for moderately rough surfaces have been
reported in the literature (Wennerberg et al. 2018).
The results suggest that presence of certain types of prostheses opposing the failed
implant(s) (conventional CRDP, conventional RPDP or FA ISFDP) may be associated
with multiple implant failure. These finding are difficult to interpret. One possible
explanation may be that patients who have experienced loss of a large number of teeth
may have presented with factors which contributed to both the loss of many teeth and the
loss of multiple implants.
5.2 Limitations of the Study
This study has some limitations. Retrospective studies are designed to analyze pre-
existing data (such as existing dental patient records in this case) and are subject to
shortcomings such as limitations of existing data set including limitations in the accuracy
of chart entries. Moreover, data gathering in this study was limited to potential variables
for which data had already been gathered.
Definitions of multiple implant failure are not consistent in the literature. This study
defined multiple implant failure as failure of more than one implant, and this approach
led to a large sample size in the multiple failure group. If a larger cut-off point for
definition of multiple implant failure had been considered (e.g., more than three failed
implants), a greater confidence in the association of identified factors could have been
achieved; however, this would have resulted in a decrease in sample size and a
diminished ability to achieve statistical significance and to draw conclusions.
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This study included implants removed due to severe bone loss in the definition of implant
failure. This complicates the interpretation of the results because implant failure and severe
bone loss may not represent the same pathophysiologic processes. Furthermore, although
implant failure is easily diagnosed via objective tests, removal of an implant with severe
bone loss involves a high degree of subjectivity. The results of this research would need to
be confirmed under the conditions of exclusion of implant loss due to severe bone loss
from the definition of implant failure and on a data set that allowed a more definitive
separation of removed implants due to failure and removed implants due to severe bone
loss.
Similar to many large-scale retrospective analyses, this study considered the patient’s
medical status as it was recorded at baseline. The shortcoming of this approach is that the
medical status of patients may change over time, and the medical status may be different
at the time of implant failure compared to the baseline. As the time interval between
initial presentation and implant failure becomes longer, the probability of a change in the
medical status from baseline increases. As baseline data was used as a basis for the
modeling incorporated in this study, the use of baseline data may not accurately reflect
the contribution of patient-related factors to implant failure and multiple implant failure.
Although the patients’ history is always updated in the health record, the most
comprehensive data set was obtained during the initial examination. The alternative
approach would have been to gather data as they were recorded at the time of implant
removal; however, assessment of the patient records indicated that the most
comprehensive data were obtained at baseline.
Older implants had machined surfaces whereas the newer contemporary implants have
improved and moderately rough surfaces. As older machined surface implants have
poorer clinical performance – especially in smokers and in poor quality bone (Balshe et
al. 2008) – and have been present in the patients’ mouths the longest, the association of
machined surface implants and multiple implant failure in this study is not surprising.
Machined implant surfaces are no longer widely used in implant dentistry, and the
applicability of this finding to the contemporary clinical setting is limited. On the other
hand, this finding validates the switch away from machined surfaces as they appear to be
more susceptible to implant failure and – as shown in this research – multiple implant
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failure.
5.3 Strengths of the Study
The current study has many advantages over the previous studies on multiple implant
failure such as a longer follow-up time, access to a large comprehensive database, and
presence of a comparison group allowing for a comparative analysis, all of which add to
the rigor of the methodology. This study is one of the very few studies on failure of
multiple dental implants and the only study that provides a comparative analysis between
failure of single and multiple dental implants. Existing literature is unclear if risk factors
for implant failure and multiple implant failure are the same, and the current research
suggests that factors associated with multiple implant failure may be distinct.
The study design of choice for assessment of burden of illness (such as failure of multiple
dental implants in the same individual) and risk analysis (such as possible influence of
proposed risk indicators on failure of multiple implants) is a cohort design, and,
historically, most studies assessing failure rates associated with dental implants and the
related risk factors have been retrospective in nature. A retrospective cohort design was
incorporated in this research instead of a prospective design due to the following reasons
(Fletcher and Fletcher 2005):
• it is the design of choice in assessment of rare diseases/outcomes (failure of
multiple implants)
• it is the optimal design for study of comorbid conditions, risk data, burden of
illness, causation and risk assessment
• practicality and ease of data gathering
• lack of concern for loss to follow-up with patients who present with the desired
outcome.
This study has a strong methodology that compares favourably with the existing cohort
studies on multiple implant failure in terms of the length of follow-up (up to 36 years)
and the presence of a comparison group. Other studies on the cluster failure phenomenon
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typically have significantly shorter follow-up durations: Weyant and Burt (2005)
followed the patients for a maximum of 5.6 years, Ekfeldt et al. (2001) had an 8-year
follow-up timeframe, and Jemt and Hager (2006) reported failures after a 3-year follow-
up. Only the study by Chrcanovic et al. (2017) had a similar follow-up duration of about
34 years.
The validity of clinical research focusing on risk factors for treatment outcomes is
contingent upon a sufficiently large sample size and a long duration of follow-up. Access
to a large and comprehensive database and allowance for a long follow-up were
advantages of conducting the study at the Faculty of Dentistry, University of Toronto.
Furthermore, retrospective design is superior in assessing a rare outcome such as failure
of multiple implants.
One of the shortcomings of existing literature is inconsistent and unclear definitions of
implant failure and multiple implant failure. This was addressed by carefully defining
implant failure and multiple implant failure in the current study. Implant failure is defined
in this research as the removal of the implant due to loss of osseointegration or significant
bone loss. Implants with significant bone loss were typically removed if the amount of
bone loss exceeded 50% in combination with either the presence of patient symptoms or
recurrent clinical signs of infection. Implants removed due to significant bone loss were
included in the current research to increase sample size and due to the fact that records
were not always clear for cases of severe bone loss whether the removed implant had
actually failed or was removed while still being osseointegrated.
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6 Recommendations for Future Research and Clinical
Practice
This research has implications for directing future clinical research and for improving
patient treatment outcomes. The results identified factors that may be associated with
multiple implant failure in comparison to single implant failure in patients with multiple
implants. Future research should focus on confirmation of these factors associated with
multiple implant failure, identification of high-risk individuals, as well as creation of
recommendations and clinical practice guidelines for management of patients with a
history of multiple implant failure. Improvements in patient selection process, treatment
planning and execution can lead to improved treatment outcomes and decreased
frequency and extent of catastrophic failures. The results of this retrospective analysis
may provide a basis for future studies and as a result, development of patient-centered
clinical practice guidelines.
7 Summary and Conclusions
Within the limitations of this study, history of implant failures, machined surfaces, post-
operative infections, presence of certain prostheses opposing the failed implant(s),
periodontitis, alcohol consumption, history of chemotherapy and use of antidepressant
medications were identified to have a possible association with failure of multiple
implants. In individuals at high-risk for multiple implant failure, it may be prudent to
consider alternative prosthodontic treatments.
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Appendices
Appendix A: Data Extraction Form
The following extraction form was utilized to systematically extract patient information from health records at the Faculty of Dentistry, University of Toronto.
Patient number
Patient age
Patient gender Male
Female
Follow-up duration Less than 1 year
1-5 years
5-10 years
10-20 years
20-30 years
30-40 years
Year of implant placement
Number of implants placed
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
Other
Number of implant(s) failed
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
100
100
16 17 18 19 20
Other
Percentage of failed implants
Location of failed implant(s)
Anterior Mx
Posterior Mx
Anterior Md
Posterior Md
Type of failure Early failure (pre-prosthetic loading)
Late failure (post-prosthetic loading)
Implant characteristics
Brand/sub-brand
Surface
Diameter
Length
Prosthesis type Full-arch IS-FDP (screw-retained)
Full-arch IS-FDP (cement-retained)
IS-OD (bar-retained)
IS OD (individual attachments)
IS-FPDP (splinted crowns)
101
101
IS-FPDP (bridge)
IS single crown
Other
Opposing occlusion Natural dentition
Full-arch IS-FDP
IS-OD
IS-FPDP
IS single crown
No opposing occlusion
Other
Loading risk/parafunction
Decreased/lack of posterior support
Bruxism
Clenching
Bone quality and quantity
Bone augmentation Yes
No
Smoking Yes
No
Diabetes Yes
No
102
102
History of periodontitis
Yes
No
History of implant failure
Yes (single)
No
Osteoporosis Yes
No
Chemotherapy Yes
No
Radiation therapy Yes
No
Depression Yes
No
Use of Antidepressants
Yes
No
Use of Bisphosphonates
Yes
No
Use of NSAIDs Yes
No
Use of Proton-pump-inhibitors
Yes
No
Use of Corticoteroids Yes
No
Use of SSRIs Yes
103
103
No
Alcohol consumption and amount
Surgeon’s experience Surgeon/specialist
Resident
Complications during surgery
None reported
Overheating
Breach of sterility
Thread exposure
Other
Primary stability Yes
No
Other (any other local or systemic factor that may potentially contribute to failure of dental implants)