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Research Article
An 11-Year Retrospective Research Study of the Predictive
Factors of Peri-Implantitis and Implant Failure:
Analytic-Multicentric Study of 1279 Implants in Peru
Frank Mayta-Tovalino ,
1
,
2
Yens Mendoza-Martiarena,
2
Percy Romero-Tapia,
2
Mar´
ıa´
Alvarez-Paucar,
2
Luis G´
alvez-Calla,
2
Juan Calder ´
on-S´
anchez,
3
Rodolfo Bolaños-Cardenas,
4
and Antonio Diaz-Sarabia
1
1
Faculty of Stomatology, Universidad Peruana Cayetano Heredia, Lima, Peru
2
Faculty of Dentistry, Universidad Nacional Mayor de San Marcos, Lima, Peru
3
Department of Stomatology, Centro Medico Naval–Marina de Guerra del Peru, Lima, Peru
4
Department of Stomatology, Instituto de Salud Oral–Fuerza A´
erea del Peru, Lima, Peru
Correspondence should be addressed to Frank Mayta-Tovalino; estadistico2.0@gmail.com
Received 23 January 2019; Revised 30 April 2019; Accepted 30 May 2019; Published 24 June 2019
Academic Editor: Tommaso Lombardi
Copyright ©2019 Frank Mayta-Tovalino et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Aim. To analyze the risk factors by logistic regression and perform the analysis of the survival rate of osseointegrated dental
implants placed in public and private institutions. Methods. An analytic-multicentric study was carried out, where 1279 dental
implants that were placed by specialists from January 2006 to October 2017 in public and private institutions (UPCH-SI, HCFAP,
CMNAVAL, UPCH-SM, and UPSJB) were evaluated. e variables sex (X1), location (X2), hypertension (X3), antibiotic
prophylaxis (X4), diabetes (X5), osteoporosis (X6), bisphosphonates (X7), history of periodontitis (X8), hypercholesterolemia
(X9), bone quality (X10), bone quantity (X11), design (X12), smoker (X13), connection (X14), edentulism type (X15), staging
(X16), 3D guided surgery (X17), load (X18), bone graft (X19), peri-implantitis (X20), mucositis (X21), and GBR (X22) were
collected and analyzed by the Kaplan–Meier survival analysis. e logit analysis was performed among all the variables to choose
the best statistical model that explains the true risk factors. e analysis was performed by multivariate logistic regression and the
Kaplan–Meier test, at a level of statistical significance of p<0.05. Results. It was found that the failure rate of the 1279 implants
evaluated was 17.98% corresponding to only 23 implants lost as they have good longevity over time. When establishing the best
multivariate logistic regression model, it was found that the variables that remained stable in relation to their statistically
significant value and more stable confidence intervals were age, osteoporosis, bisphosphonates, history of periodontitis, bone
quality, bone graft, connection, number of implants, GBR (guided bone regeneration), and follow-up. Conclusions. Dental
implants placed by specialists in public and private institutions had a failure rate similar to that in studies previously published in
other countries.
1. Introduction
e main challenge of oral implantology is to achieve the
functionality of the implants; however, osseointegration is
associated with several factors, such as the reduction of
surgical trauma, the shortening of treatment time, and the
improved preservation of surrounding bone and soft tissue.
In cases with sufficient primary stability, the literature
reports that well-planned implant placement produces high
efficacy in terms of long-term success and aesthetic result
[1, 2].
For this reason, the treatment with dental implants is an
integral part of the patient’s rehabilitation. Predictability is
very important because it is a great advantage of dental
implant therapy which is well documented for partially and
completely edentulous patients. With the growing demand
Hindawi
International Journal of Dentistry
Volume 2019, Article ID 3527872, 8 pages
https://doi.org/10.1155/2019/3527872
and interest on the part of the populations, it is essential to
establish the optimal care and maintenance that should be
given to the placed implants [3–11]. As a consequence of this
situation, studies are needed to evaluate the prognosis and
long-term functionality of dental implants, reporting sur-
vival rates. However, many studies describe only the survival
of implants in favorable places without assuming the risk
factors that could be adverse to the survival of this alternative
treatment. In addition, most of the reports assessing risk
factors for failure are deficient in terms of their statistical
analyses since the potentials of the risk factors for failure
should be determined using appropriate statistical tech-
niques that support the statistically significant weight
through the establishment of models to verify this condition
[12].
In addition, survival rates do not take into account the
presence of surgical, biological, and prosthetic complica-
tions; despite the remarkable survival rate of dental implants,
there are an increasing number of patients with peri-implant
diseases [13]. Given the possible systemic ramifications of
chronic inflammation, it is essential to better understand the
prevalence of peri-implant diseases and the risk factors to be
able to prevent or treat this injury that affects the sur-
rounding tissues. Currently, there is controversy regarding
the therapeutic management of peri-implant diseases be-
cause they can cause various discomforts with a surgical or
nonsurgical treatment which is not necessarily protocolized
[14]. erefore, these pathologies have negative impacts on
systemic health or eventual loss of the implant [15]. e
determination of future peri-implant diseases is necessary
for clinical decision-making.
us, the purpose of this analytic-multicentric study was
to analyze the risk factors by logistic regression and perform
the analysis of the survival rate of osseointegrated dental
implants placed in public and private institutions.
2. Materials and Methods
2.1. Subject Population. e study design was analytical,
correlational, comparative, and retrospective. e unit of
analysis comprised patients with a dental implant from the
Central Hospital of the Peruvian Air Force (HCFAP), Naval
Medical Center (CMNAVAL), Universidad Peruana Caye-
tano Heredia (UPCH) at its headquarters San Martin de
Porres and San Isidro, and Universidad Privada San Juan
Bautista (UPSJB). We worked with the entire population
that had implants placed in public and private institutions
for up to 11 years of evolution (N�1279).
2.2. Ethical Considerations. For the execution of the study,
authorization was requested to the Ethics Committee of the
Universidad Peruana Cayetano Heredia approved with code
SIDISI 100839, and permission was also requested to the
other institutions. No risks or conflicts of interest were
anticipated since the research was retrospective that used the
clinical histories of the Periodontics and Implantology
service of the aforementioned institutions. is research was
carried out following the STROBE (Strengthening the
Reporting of Observational Studies in Epidemiology)
guidelines.
(i) Inclusion criteria:
(1) Patients of both sexes aged 18 to 80 years
(2) Patients with controlled systemic diseases
(3) Patients from hospitals of the armed forces and
private universities that authorize the execution
of the study
(4) Patients with clinical histories that at least have
the main variables (age, sex, location of the
implant, hypertension, antibiotic prophylaxis,
bone quality, bone quantity, diabetes, osteopo-
rosis, bisphosphonates, history of periodontitis,
and hypercholesterolemia)
(ii) Exclusion criteria:
(1) Patients with illegible clinical histories
(2) Patients with clinical histories that last less than
1 year or high after installation of their implants
2.3. Risk Factor Examinations. e risk factors associated
with the failure of the implant were evaluated in the fol-
lowing categories: peri-implant health status (which was
evaluated through the radiographic and clinical study
through the probing recorded in the specialized histories of
the Periodontics and Implantology service) and the variables
such as sex (X1), location (X2), hypertension (X3), antibiotic
prophylaxis (X4), diabetes (X5), osteoporosis (X6),
bisphosphonates (X7), history of periodontitis (X8), hy-
percholesterolemia (X9), bone quality (X10), bone quantity
(X11), design (X12), smoker (X13), connection (X14),
edentulism type (X15), staging (X16), 3D guided surgery
(X17), load (X18), bone graft (X19), peri-implantitis (X20),
mucositis (X21), and guided bone regeneration (GBR)
(X22). All these covariates were obtained from the clinical
records of the institutions.
2.4. Statistical Analysis. To perform the descriptive statistics
of the variables’ risk factors, survival and failure of implants,
we proceeded to obtain the univariate analysis through the
measures of central tendency (mean and standard deviation)
of the quantitative variables and frequencies and percentages
of the qualitative variables. To determine normality, the
Shapiro–Wilk test was used. Finally, the multivariate anal-
ysis was performed by logistic regression, establishing the
best statistical model that explains the influence of risk
factors, and then, the analysis of survival and failure of the
implants was made through the Kaplan–Meier method.
3. Results
3.1. Systemic Risk Factors. When analyzing the descriptive
characteristics of the systemic risk factors of the osseoin-
tegrated dental implants placed in five public and private
institutions of this analytic-multicentric study, it was found
that the most predominant sex was female at the UPCH-SM
headquarters with 111 (50.9%), while the location was
2International Journal of Dentistry
primarily presented in the mandible at the CMNAVAL
headquarters with 77 (37.3%); however, in all institutions, an
antibiotic prophylaxis protocol (413 (100%)) was applied. In
relation to diabetes and osteoporosis, it was presented
mostly at the UPCH-SM headquarters with 3 (20%) and 1
(1.8%), respectively. On the contrary, the presence of
bisphosphonate consumption was only found in 5 (100%) of
patients at the UPCH-SI headquarters. Finally, the history of
periodontitis and hypercholesterolemia was increased at
CMNAVAL with 49 (43.3%) and 21 (50%), respectively
(Table 1).
3.2. Surgical Risk Factors. It was found that the predominant
bone quality was type II (132 (38.2%)), the bone quantity was
type B (82 (33.7%)), the hybrid design was the most prev-
alent (123 (43.6%)), and the Morse connection was the most
used (116 (55.5%)). However, the variables smoking habit,
3D guided surgery, type of edentulism, and type of pros-
thetic load had low prevalences in most institutions. Oth-
erwise, bone grafts and GBR were more prevalent at UPCH-
SI (86 (6.4%)) and CMNAVAL (46 (36.5%)), respectively
(Table 2).
3.3. Multivariate Logistic Regression Model. When estab-
lishing the best multivariate logistic regression model to
analyze the influence of each risk factor on the success and
survival of osseointegrated implants, it was found that the
variables that remained stable in relation to their statistically
significant value and more stable confidence intervals were
age, osteoporosis, bisphosphonates, history of periodontitis,
bone quality, bone graft, connection, number of implants,
GBR, and follow-up. However, only the variables age, os-
teoporosis, history of periodontitis, bone quality, number of
implants, GBR, and follow-up had ORs really considered as
risk factors (Table 3).
3.4. Implant Failure Rate and Implant Survival Rate. It was
found that the failure rate of the 1279 implants evaluated was
17.98% corresponding to only 23 implants lost, so it can be
inferred that the implants are a good treatment alternative
given that they have good longevity over time (Table 4). e
evaluation of the 11-year cumulative survival of the
osseointegrated implants showed that the survival rate was
inversely proportional to time in years. During the first and
the second year, a rate of 99.4% was found, while at eleven to
twelve years, it was reduced to 37.8% (Table 5).
3.5. Distribution of Implant Survival according to
Headquarters. Finally, quantifying the survival rate of the
osseointegrated implants for the five institutions evaluated
in this multicentric study showed that, in all the institutions
(UPCH-SI, HCFAP, CMNAVAL, UPSJB, and UPCH-SM),
the rate was above 96.5%; however, the survival rate of dental
implants continues to decrease progressively as the years of
functionality in the oral cavity increase (Table 6) (Figure 1).
4. Discussion
In recent years, there is a great demand from patients who
are treated by dental implants to rehabilitate the edentulous
areas. erefore, the effect of certain factors on the loss of
implants, which generate a greater risk of predisposing to
marginal peri-implant bone loss, should be considered. To
evaluate the survival of the implants, it is necessary to
identify the presence of complications. If reported, the time
of implant loss is described as early (before prosthetic
loading) or late (after prosthetic loading). It is agreed that
survival is expressed in cumulative survival rates, where a
successful implant refers to the presence of an implant in the
absence of both biological and prosthetic complications [15].
e currently accepted dental implant planning protocol
includes clinical examination, diagnostic tests, and clinical
history, to establish treatment options, and an appropriate
maintenance phase. e placement of the implants is con-
ditioned to the usual restrictions for minor surgery that are
established by the systemic conditions of the patient. It
should be noted that these care protocols vary according to
the work philosophy that is handled in each institution;
therefore, the level of evidence should help identify possible
risk factors that act as contraindications for implant therapy
[16]. ere is little literature that has analyzed the effect of
multiple risk factors on implant survival, especially in elderly
patients who have some chronic systemic disease, have a
history of smoking, and consume a drug that could com-
promise osseointegration.
Consequently, with the results of this investigation, it
was found that the long-term survival rate of the implants is
a tool for objective measurement of the stability of the
implants which is 99.4%, which was similar to that described
by Cosyn et al. [17] (96.5%), Balshi et al. [18] (99.8%), and
Moreira Melo et al. [19] (92.65%). e overall success rate
was above 95%, which is in agreement with other studies that
reported values ranging from 81% to 93% [20–22]. A clear
example of an investigation that confirms through a mul-
tivariate model similar results is that described by Borba
et al. [23] who found that the survival rate was 91.8%. e
multivariate GEE analysis revealed that a significant risk
factor for the implant failure was the implant in the max-
illary area (p�0.014), and the bone graft seemed to be a risk
factor for implant failure (p�0.054). GEE analyses showed
that maxillary implants had significantly worse results in this
population and were considered a risk factor for implant
failure. erefore, our results suggested that implants placed
in an area of bone augmentation, age, bone quality, and GBR
had a tendency to fail, as demonstrated by a logit model.
Otherwise, in relation to the failure of the implants,
according to the results described by Mohajerani et al. [24],
of the 1093 implants evaluated, only 73 cases (6.68%) failed
during the early stages of osseointegration. e groups were
significantly different in terms of implant surface, placement
in a new alveolus, use of an antibiotic prophylactic, and bone
density (p<0.05). However, age, sex, height of the implant,
type of the implant (cylindrical or conical), and placement in
one or two stages were not significantly different between the
two groups (p<0.05). ese results differ from the data
International Journal of Dentistry 3
Table 2: Surgical risk factors of osseointegrated dental implants.
Factors Category UPCH-SI,
n(%)
HCFAP,
n(%)
CMNAVAL,
n(%)
UPSJB,
n(%)
UPCH-SM,
n(%)
Total,
n(%)
Bone quality (X10)
Type I 2 (7.4) 0 7 (25.9) 1 (3.7) 17 (62.9) 27 (100)
Type II 36 (10.4) 32 (9.8) 119 (34.4) 26 (7.5) 132 (38.2) 345
(100)
Type III 28 (49.1) 9 (15.7) 17 (29.8) 2 (3.5) 1 (1.7) 57 (100)
Type IV 1 (50) 0 0 0 1 (50) 2 (100)
Bone quantity (X11)
A 0 1 (4.5) 6 (27.2) 1 (4.5) 14 (63.6) 22 (100)
B 47 (19.3) 33 (13.5) 68 (27.9) 13 (5.3) 82 (33.7) 243
(100)
C 18 (12.1) 7 (4.7) 69 (46.6) 14 (9.4) 40 (27.0) 148
(100)
Design (X12)
Conical 6 (7.32) 0 38 (46.3) 27
(32.9) 11 (13.4) 82 (100)
Cylindrical 20 (29.8) 14 (20.9) 16 (23.8) 0 17 (25.3) 67 (100)
Hybrid 41 (14.5) 27 (9.5) 89 (31.5) 2 (0.7) 123 (43.6) 282
(100)
Smoker (X13) Not present 60 (14.1) 41 (9.6) 143 (33.7) 29 (6.8) 151 (35.6) 424
(100)
Present 7 (100) 0 0 0 0 7 (100)
Connection (X14)
Internal 7 (3.2) 40 (18.6) 130 (60.4) 3 (1.4) 35 (16.2) 215
(100)
External 6 (85.7) 0 18 (14.2) 0 0 7 (100)
Morse cone 54 (25.8) 1 (0.4) 12 (5.7) 26 (12.4) 116 (55.5) 209
(100)
Type of edentulism
(X15)
Total 3 (21.4) 0 0 0 11 (78.5) 14 (100)
Partial 64 (15.3) 41 (9.8) 143 (34.2) 29 (6.9) 140 (33.5) 417
(100)
Staging (X16)
First stage 1 (9.0) 0 3 (27.2) 6 (54.5) 1 (9.0) 11 (100)
Second stage 66 (15.7) 41 (9.7) 140 (33.3) 23 (5.4) 150 (35.7) 420
(100)
Table 1: General risk factors of osseointegrated dental implants.
Public and private institutions
Factors Category UPCH-SI,
n(%)
HCFAP,
n(%) CMNAVAL, n(%) UPSJB, n(%) UPCH-SM, n(%) Total, n(%)
Sex (X1) Female 50 (22.9) 15 (6.8) 29 (13.3) 13 (5.9) 111 (50.9) 218 (100)
Male 17 (7.9) 26 (12.2) 114 (53.5) 16 (7.5) 40 (18.7) 213 (100)
Location (X2) Jaw 27 (13.1) 21 (10.19) 77 (37.3) 15 (7.2) 66 (32.0) 216 (100)
Maxilla 40 (17.7) 20 (8.8) 66 (29.3) 14 (6.2) 85 (37.7) 225 (100)
Hypertension (X3) Not present 53 (14.6) 28 (7.7) 124 (34.1) 23 (6.3) 135 (37.1) 363 (100)
Present 14 (20.5) 13 (19.1) 19 (27.9) 6 (8.8) 16 (23.5) 68 (100)
Antibiotic therapy (X4) Not present 0 0 0 0 0 0
Present 67 (15.5) 41 (9.5) 143 (33.1) 29 (6.7) 151 (35.0) 431 (100)
Diabetes (X5) Not present 67 (15.9) 41 (9.7) 138 (32.7) 27 (6.4) 148 (35.1) 421 (100)
Present 0 0 5 (50) 2 (20) 3 (20) 10 (100)
Osteoporosis (X6) Not present 57 (13.6) 40 (9.5) 143 (34.1) 29 (6.9) 150 (35.8) 419 (100)
Present 10 (83.3) 1 (8.3) 0 0 1 (8.3) 12 (100)
Bisphosphonates (X7) Not present 62 (14.5) 41 (9.6) 143 (33.5) 29 (6.8) 151 (35.4) 426 (100)
Present 5 (100) 0 0 0 0 5 (100)
History of periodontitis (X8) Not present 63 (19.8) 21 (6.6) 94 (29.5) 17 (5.2) 123 (38.6) 318 (100)
Present 4 (3.5) 20 (17.7) 49 (43.3) 12 (10.6) 28 (24.7) 113 (100)
Hypercholesterolemia (X9) Not present 67 (17.2) 40 (10.2) 122 (31.3) 23 (5.9) 137 (35.2) 369 (100)
Present 0 1 (2.3) 21 (50) 6 (14.2) 14 (33.3) 42 (100)
UPCH-SI: Universidad Peruana Cayetano Heredia, San Isidro; HCFAP: Hospital Central de la Fuerza A´erea del Per´u; CMNAVAL: Centro Medico Naval;
UPSJB: Universidad Privada San Juan Bautista; UPCH-SM: Universidad Peruana Cayetano Heredia, San Martin de Porres.
4International Journal of Dentistry
obtained in the present study given that if they were found in
relation to their statistically significant value and more stable
confidence intervals in the factors age, osteoporosis,
bisphosphonates, history of periodontitis, bone quality, bone
Table 3: Multivariate logistic regression model of each risk factor on the success and survival of osseointegrated implants.
Independent variables OR p95% CI
Age (X0) 1.0 0.122 0.98–1.10
Sex (X1) 0.9 0.879 0.23–3.48
Location (X2) 0.5 0.312 0.13–1.88
Hypertension (X3) 0.5 0.463 0.11–2.72
Antibiotic therapy (X4) — — —
Diabetes (X5) 5.6 0.167 0.48–65.9
Osteoporosis (X6) 44.8 0.011 2.40–834.8
Bisphosphonates (X7) 0.09 0.205 0.00–3.65
History of periodontitis (X8) 3.1 0.082 0.86–11.31
Hypercholesterolemia (X9) 5.1 0.046 1.02–25.44
Bone quality (X10) 5.8 0.021 1.30–26.6
Bone quantity (X11) 0.5 0.268 0.15–1.69
Design (X12) 1.5 0.388 0.57–4.16
Smoker (X13) 1.6 0.798 0.04–60.4
Connection (X14) 0.4 0.038 0.22–0.95
Type of edentulism (X15) 0.4 0.527 0.03–5.26
Staging (X16) — — —
3D surgery (X17) — — —
Load (X18) — — —
Bone graft (X19) 0.1 0.183 0.01–2.16
GBR (X22) 24.1 0.007 2.34–249.6
Follow-up (X23) 1.7 0.000 1.35–2.31
Number of implants (X24) 1.1 0.290 0.90–1.39
OR: odds ratio; CI: confidence interval; GBR: guided bone regeneration. Logit model: all the variables were entered in the statistical analysis of the multivariate
model. e logit model showed that age, sex, location of the implant, antibiotic therapy, diabetes, bisphosphonates, history of periodontitis, bone quantity,
implant design, smoking habit, type of edentulism, bone graft, and number of implants placed were not factors of statistically significant risk in the general
logistic model for the failure of the implants (p<0.05).
Table 4: Rate of failure of osseointegrated implants in eleven years.
Implants Lost Failure rate (%) 95% CI
Failure 1279 23 17.98 11.95–27.06
Table 2: Continued.
Factors Category UPCH-SI,
n(%)
HCFAP,
n(%)
CMNAVAL,
n(%)
UPSJB,
n(%)
UPCH-SM,
n(%)
Total,
n(%)
3D surgery (X17) Not present 67 (15.5) 41 (9.5) 143 (33.1) 29 (6.7) 151 (35.0) 431
(100)
Present 0 0 0 0 0 0
Load (X18)
Early 0 0 7 (41.1) 6 (35.2) 4 (23.5) 17 (100)
Conventional 67 (16.1) 41 (9.9) 136 (32.8) 23 (5.5) 147 (35.5) 414
(100)
Bone graft (X19)
Not present 59 (19.2) 35 (11.4) 95 (31.0) 14 (4.5) 103 (33.6) 306
(100)
Present 86 (6.4) 6 (4.8) 48 (38.4) 15 (12.0) 48 (38.4) 125
(100)
Peri-implantitis (X20) Not present 59 (14.4) 40 (9.8) 134 (32.8) 27 (6.6) 148 (36.2) 408
(100)
Present 8 (34.7) 1 (4.3) 9 (39.1) 2 (8.7) 3 (13.0) 23 (100)
Mucositis (X21) Not present 58 (14.2) 40 (9.8) 135 (33.0) 27 (6.6) 146 (36.2) 408
(100)
Present 9 (39.1) 1 (4.3) 8 (34.7) 2 (8.7) 3 (13.0) 23 (100)
GBR (X22)
Not present 51 (16.7) 34 (11.1) 97 (31.6) 16 (5.2) 107 (35.0) 305
(100)
Present 16 (12.7) 7 (5.5) 46 (36.5) 13 (10.3) 44 (34.9) 126
(100)
International Journal of Dentistry 5
graft, connection, number of implants, GBR, and follow-up
(p<0.05). Probably, the discrepancies can be attributed to
certain variables that are difficult to control such as diet,
race, and hygiene habits that may be influencing the survival
of dental implants.
On the contrary, when looking for scientific evidence,
no literature was found that mathematically established a
regression model on the risk factors of dental implants in
Peru. erefore, the present investigation opens a large
line of research because by statistically determining these
factors, the prevalence of biological complications of
osseointegrated implants can be reduced. Public and
private institutions would probably have a great accep-
tance in rethinking their surgical and prosthetic protocols
when planning the placement of dental implants and thus
be able to provide a therapy that has high predictability
and thus guarantee a success rate of dental implants in
Peru.
Table 5: Evaluation of the cumulative survival in 11 years of osseointegrated implants.
Time (years) Implants Failure Survival rate (%) 95% CI
1-2 431 2 99.4 97.8–99.8
2-3 320 3 98.3 96.0–99.3
3-4 217 5 95.4 91.5–97.6
4-5 117 2 93.6 88.5–96.4
5-6 88 5 87.0 78.6–92.2
6-7 49 4 78.9 67.2–86.8
7-8 33 1 75.7 62.5–84.8
8-9 16 0 75.7 62.5–84.8
9-10 5 0 75.7 62.5–84.8
10-11 2 1 37.8 1.6–79.3
11-12 1 0 37.8 1.6–79.3
e Kaplan–Meier method is used to determine the survival rate of the implant. e analysis was performed on 1279 osseointegrated implants during 11 years
of functionality.
Table 6: Distribution of implant survival by headquarters.
Institution Time (years) Implants Failure Survival rate (%) 95% CI
UPCH-SI
1 67 0 100 —
2 56 1 98.2 87.9–99.7
3 40 1 95.7 83.8–98.9
4 23 1 91.6 74.4–97.4
5 19 3 77.1 54.3–89.5
6 6 2 51.4 19.0–76.5
HCFAP
1 41 0 100 —
2 14 0 100 —
3 7 0 100 —
4 4 0 100 —
6 1 1 0.00 —
CMNAVAL
1 143 1 99.3 95.1–99.9
2 93 1 98.2 92.9–99.57
3 62 4 91.9 82.3–96.3
4 4 0 91.9 82.3–96.3
5 3 1 61.2 82.3–96.3
7 2 1 30.6 80.0–90.9
10 1 1 0.00 1.0–73.6
UPSJB
1 29 1 96.5 77.9–99.5
2 14 1 89.6 62.1–97.5
3 4 0 89.6 62.1–97.5
7 1 0 89.6 62.1–97.5
UPCH-SM
1 151 0 100 —
2 143 0 100 —
3 104 0 100 —
4 85 1 98.8 91.9–99.8
5 64 1 97.2 89.0–99.3
6 39 1 94.7 83.8–98.3
7 30 0 94.7 83.8–98.3
8 15 0 94.7 83.8–98.3
9 4 0 94.7 83.8–98.3
11 1 0 94.7 83.8–98.3
6International Journal of Dentistry
e biggest limitation of this research was that the current
literature has been evaluated on the risk factors for implants
without necessarily establishing a regression model that
statistically endorses the risk factors that actually significantly
influence the failure of the implants. In addition, most of the
research is in association with many confounding factors;
numbers of subcategories can often also vary a statistically
significant comparison, and the follow-up of the implants in
the times varies and is often short-term. ere are many risk
factors that are potential and require that the clinician have a
wide knowledge and understanding of these factors to discuss
them with each patient and consider them in the planning and
treatment of dental implants.
Another limitation of this study was that it only retro-
spectively evaluated some predictors such as age, sex, type of
the implant, surface, length, diameter, location, bone quality,
and bone quantity that could influence the survival rate
among various factors that are related, in comparison with
other investigations, with the survival of the implant. In
addition, there were several factors related to systemic
diseases that could influence the survival rate. Finally, several
factors combined could determine the survival rate, but they
were not analyzed in this study. It is recommended to carry
out well-controlled long-term prospective studies, which
confirm the relationship between the factors that influence
the survival rate in order to be analyzed consecutively. When
many investigations draw a common conclusion about the
reason for implant failure, common factors related to the
survival rate will have a greater influence on failure;
therefore, more studies are needed on them. For this reason,
it is suggested to perform long-term longitudinal studies that
complement the evidence on which are the true risk factors
that continue to influence the failure of dental implants in a
Peruvian population.
5. Conclusions
In conclusion, it was found that, in the 1279 osseointegrated
implants of this multicentric study, when establishing the
best multivariate logistic regression model, the variables that
remained stable in relation to their statistically significant
value and confidence intervals were age, osteoporosis,
bisphosphonates, history of periodontitis, bone quality, bone
graft, connection, number of implants, GBR, and follow-up.
In addition, it was shown that the failure rate was only
17.98% corresponding to only 23 implants lost, observing
that the survival rate is inversely proportional to time in
years and reporting that, during the first and the second year,
a rate of 99.4% was found, while at eleven to twelve years, it
was reduced to 37.8%. Finally, it was shown that, in all the
institutions (UPCH-SI, HCFAP, CMNAVAL, UPSJB, and
UPCH-SM), the success rate was above 96.5%.
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
Acknowledgments
e authors wish to thank the institutions that provided the
facilities for the execution of this study: head of the Instituto
de Salud Oral of the Air Force from Peru (ISOFAP), head of
the Department of Stomatology of the Centro Medico Naval
of the Navy of Peru, Universidad Privada San Juan Bautista,
and Universidad Peruana Cayetano Heredia.
References
[1] F. G. Mangano, C. Mangano, M. Ricci, R. L. Sammons,
J. A. Shibli, and A. Piattelli, “Esthetic evaluation of single-
tooth Morse taper connection implants placed in fresh ex-
traction sockets or healed sites,” Journal of Oral Implantology,
vol. 39, no. 2, pp. 172–181, 2013.
[2] H. Huang, D. Wismeijer, X. Shao, and G. Wu, “Mathematical
evaluation of the influence of multiple factors on implant
stability quotient values in clinical practice: a retrospective
study,” erapeutics and Clinical Risk Management, vol. 12,
pp. 1525–1532, 2016.
[3] G. A. Zarb and A. Schmitt, “e longitudinal clinical effec-
tiveness of osseointegrated dental implants in anterior par-
tially edentulous patients,” International Journal of
Prosthodontics, vol. 6, no. 2, pp. 180–188, 1993.
[4] G. A. Zarb and A. Schmitt, “e longitudinal clinical effec-
tiveness of osseointegrated dental implants in posterior
partially edentulous patients,” International Journal of Pros-
thodontics, vol. 6, no. 2, pp. 189–196, 1993.
[5] A. Schmitt and G. Zarb, “e longitudinal clinical effective-
ness of osseointegrated dental implants for single-tooth re-
placement,” International Journal of Prosthodontics, vol. 6,
no. 2, pp. 197–202, 1993.
[6] G. A. Zarb and A. Schmitt, “e longitudinal clinical effec-
tiveness of osseointegrated dental implants: the Toronto
study. Part I: surgical results,” Journal of Prosthetic Dentistry,
vol. 63, no. 4, pp. 451–457, 1990.
[7] J. S. Feine, G. E. Carlsson, M. A. Awad et al., “e McGill
consensus statement on overdentures. Montreal, Quebec,
0.00
0.25
0.50
0.75
1.00
0 5 10
Analysis time
HCFAP
UPSJB
UPCH-SI
CMNAVAL
UPCH-SM
Kaplan–Meier survival estimates
Figure 1: Survival rate according to the time for the five
institutions.
International Journal of Dentistry 7
Canada. May 24-25, 2002,” International Journal of Pros-
thodontics, vol. 15, no. 4, pp. 413-414, 2002.
[8] G. A. Zarb and A. Schmitt, “e longitudinal clinical effec-
tiveness of osseointegrated dental implants: the Toronto
study. Part III: problems and complications encountered,”
Journal of Prosthetic Dentistry, vol. 64, no. 2, pp. 185–194,
1990.
[9] G. A. Zarb and A. Schmitt, “e longitudinal clinical effec-
tiveness of osseointegrated dental implants: the Toronto
study. Part II: the prosthetic results,” Journal of Prosthetic
Dentistry, vol. 64, no. 1, pp. 53–61, 1990.
[10] C. W. Douglass and A. J. Watson, “Future needs for fixed and
removable partial dentures in the United States,” Journal of
Prosthetic Dentistry, vol. 87, no. 1, pp. 9–14, 2002.
[11] J. Rustemeyer and A. Bremerich, “Patients’ knowledge and
expectations regarding dental implants: assessment by
questionnaire,” International Journal of Oral and Maxillofa-
cial Surgery, vol. 36, no. 9, pp. 814–817, 2007.
[12] E. Anitua, G. Orive, J. Aguirre, B. Ardanza, and I. And´
ıa, “5-
year clinical experience with BTI®dental implants: risk
factors for implant failure,” Journal of Clinical Periodontology,
vol. 35, pp. 724–732, 2008.
[13] O. C. Koldsland, A. A. Scheie, and A. M. Aass, “Prevalence of
peri-implantitis related to severity of the disease with different
degrees of bone loss,” Journal of Periodontology, vol. 81, no. 2,
pp. 231–238, 2010.
[14] P. Rosen, D. Clem, D. Cochran et al., “Peri-implant mucositis
and peri-implantitis: a current understanding of their di-
agnoses and clinical implications,” Journal of Periodontology,
vol. 84, no. 4, pp. 436–443, 2013.
[15] L. J. Heitz-Mayfield and G. Huynh-Ba, “History of treated
periodontitis and smoking as risks for implant therapy,”
International Journal of Oral & Maxillofacial Surgery, vol. 24,
pp. 39–68, 2009.
[16] A. Mombelli and N. Cionca, “Systemic diseases affecting
osseointegration therapy,” Clinical Oral Implant Research,
vol. 17, no. S2, pp. 97–103, 2006.
[17] J. Cosyn, E. Vandenbulcke, H. Browaeys, G. Van Maele, and
H. De Bruyn, “Factors associated with failure of surface-
modified implants up to four years of function,” Clinical
Implant Dentistry and Related Research, vol. 14, no. 3,
pp. 347–358, 2012.
[18] T. Balshi, G. Wolfinger, B. Stein, and S. Balshi, “A long-term
retrospective analysis of survival rates of implants in the
mandible,” International Journal of Oral & Maxillofacial
Implants, vol. 30, no. 6, pp. 1348–1354, 2015.
[19] A. C. Moreira Melo, A. R. Andrighetto, S. D. Hirt,
A. L. M. Melo, S. Uhrigshardt Silva, and M. Duarte Silva, “Risk
factors associated with the failure of miniscrews—a ten-year
cross sectional study,” Brazilian Oral Research, vol. 30, no. 1,
pp. 1–6, 2016.
[20] J. Antoszewska, M. A. Papadopoulos, H.-S. Park, and
B. Ludwigk, “Five-year experience with orthodontic mini-
screw implants: a retrospective investigation of factors
influencing success rates,” American Journal of Orthodontics
and Dentofacial Orthopedics, vol. 136, no. 2, pp. 158.e1–
158.e10, 2009.
[21] A. Manni, M. Cozzani, F. Tamborrino, S. De Rinaldis, and
A. Menini, “Factors influencing the stability of miniscrews: a
retrospective study on 300 miniscrews,” European Journal of
Orthodontics, vol. 33, no. 4, pp. 388–395, 2011.
[22] S. Cheng, I. Tseng, J. Lee, and S. Kok, “A prospective study of
the risk factors associated with failure of mini-implants used
for orthodontic anchorage,” International Journal of Oral &
Maxillofacial Surgery, vol. 19, no. 1, pp. 100–106, 2004.
[23] M. Borba, D. Deluiz, E. Lourenço, L. Oliveira, and P. Tannure,
“Risk factors for implant failure: a retrospective study in an
educational institution using GEE analyses,” Brazilian Oral
Research, vol. 21, no. 31, pp. 1–6, 2017.
[24] H. Mohajerani, R. Roozbayani, S. Taherian, and R. Tabrizi,
“e risk factors in early failure of dental implants: a retro-
spective study,” Journal of Dentistry, vol. 18, no. 4, pp. 298–
303, 2017.
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