jomiimplants€¦ · the international journal of oral & maxillofacial implants s5 introduction...

Vol 34/2019 Supplement

The International Journal of

Oral & Maxillofacial ImplantsJOMI

Summit ChairClark M. Stanford, DDS, PhD

August 8–10, 2018 Oak Brook Hills Resort

Oak Brook, Illinois

OPTIMIZED PATIENT CARE: Converting Osseointegration

to Patientintegration

Introduction

s5 Optimized Patient Care: Converting Osseointegration to Patientintegration Clark M. Stanford

Group 1 Primary Stability and Osseointegration

s6 Group Participants and Observerss7 Relationship Between Primary/Mechanical and Secondary/Biological

Implant Stability Alberto Monje/Andrea Ravidà/Hom-Lay Wang/Jill A. Helms/John B. Brunski

Group 2 Inflammation and Osseointegration

s24 Group Participants and Observerss25 Peri-implant Mucosal Tissues and Inflammation: Clinical Implications Joseph P. Fiorellini/Kevin WanXin Luan/Yu-Cheng Chang/David Minjoon Kim/Hector L. Sarmiento

Group 3 Systemic Health, Medications, and Osseointegration

s34 Group Participantss35 The Effects of Systemic Diseases and Medications on Implant Osseointegration:

A Systematic Review Tara Aghaloo/Joan Pi-Anfruns/Alireza Moshaverinia/Danielle Sim/Tristan Grogan/Danny Hadaya

The International Journal ofORAL & MAXILLOFACIAL IMPLANTSTable of Contents

Volume 34 • Supplement • 2019

ISSN 0882-2786 (print) • ISSN 1942-4434 (online)

Academy of Osseointegration 2018 Summit

Publisher H. W. Haase

Executive Vice President William G. Hartman

Director, Journal Publications Lori A. Bateman

Managing Editor Julie Rule

Production Manager Diane Curran

Production Angela Kreeger

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copying, recording, or any information and retrieval system, without permission in writing from the pub-lisher. The views expressed herein are those of the individual authors and are not necessarily those of the publisher or the Academy of Osseointegration (AO). While the information in this issue of JOMI is believed to be true and accurate, neither the JOMI Editors nor Publisher accept legal responsibility for any errors or omissions that may have been made. Articles were reviewed by the participants of the Summit but did not undergo the regular JOMI peer review process. Information included herein is not professional advice and is not intended to replace the judgment of a practitioner with respect to particular patients, procedures, or practices. To the extent permissible under applicable laws, the publisher and AO disclaim responsibility for any injury and/or damage to persons or property as a result of any actual or alleged libelous statements, infringement of intellectual property or other propri-etary or privacy rights, or from the use or operation of any ideas, instructions, procedures, products, or methods contained in the material therein. This disclaimer also includes the content of websites of other organizations, companies, or individuals to which the published material may be linked.

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The International Journal of Oral & Maxillofacial Implants s5

INTRODUCTION

Optimized Patient Care: Converting Osseointegration to Patientintegration

The Academy of Osseointegration (AO) is the global leader in research and clinically rele-vant information relative to oral implant and associated care for optimal outcomes. In addi-tion to a series of meetings per year focused on education and dissemination, the Academy in 1996 began holding a Workshop or Summit every 4 years. The driving question behind the purpose of this Summit, held August 8–10, 2018 in Oak Brook, IL, was to determine if there may be elevated risk factors attributable to local or systemic issues, suggested by recent literature.

This 3-day workshop was the culmination of dedicated work by 70 international clinician-scientists working in three teams under the guidance of three chairpersons. There were also professional member dental organizations represented, including the Ameri-can Association of Oral and Maxillofacial Surgeons (AAOMS), the American Academy of Periodontology (AAP), and the American College of Prosthodontists (ACP).

The Summit received strong financial and participatory support from the Academy of Osseointegration Foundation (AOF) along with strong financial support from many corpo-rate friends of the AO. The AO board and the membership of the Academy sincerely thank both the AOF and our corporate friends for their support. The AOF content experts and our corporate leaders in the fields of implant therapy and biologics listened and learned as all the content experts debated the three topic areas.

Participants discussed three questions for the Summit, each researched and presented by one of three working groups. Framed around outcomes or impact on long-term os-seointegration, the questions addressed: Primary Stability and Osseointegration (Group 1); Inflammation and Osseointegration (Group 2); and Systemic Health, Medications, and Osseointegration (Group 3). Leading the groups were Drs John B. Brunski (Biomaterial/Bioengineering), Stanford University, CA (Group 1); Joseph P. Fiorellini (AO Director and Periodontist), University of Pennsylvania, Philadelphia, PA (Group 2); and Tara L. Aghaloo (AO Secretary and Associate Professor, Oral and Maxillofacial Surgery), UCLA, Los Angeles, CA (Group 3). Prior to the meeting, the three teams built formal systematic reviews on the three topic areas and came prepared to debate within the general session. After an initial breakout session, each of the chairs provided an overview of their group’s outcomes, fol-lowed by a 60-minute plenary presenter and a clinical presentation for the entire audience. A moderated and dynamic discussion followed each plenary speaker and the clinical case presentation. The plenary presenters were Dr Rick Sumner, Mary Lou Bell McGrew Presi-dential Professor for Medical Research and Chair of the Department of Cell and Molecular Medicine at Rush University Medical Center in Chicago (Group 1); Dr Flavia Teles, Associate Professor, Department of Microbiology, University of Pennsylvania School of Dental Medi-cine (Group 2); and Dr Susan Bukata, Orthopedic Surgeon, Ronald Reagan UCLA Medical Center, Department of Orthopedic Surgery, Santa Monica, CA (Group 3).

Following the meeting, the outcomes of the debates were incorporated into the final three papers published in this special supplemental edition of JOMI as a service to the AO membership. Thank you to all of the experts who worked very hard in preparing for this Summit and the AOF and Corporate friends of the AO who supported this important con-tribution to patient care.

Clark M. Stanford, DDS, PhDSummit Chair & AO President-ElectDistinguished Professor and DeanCollege of DentistryUniversity of Illinois at Chicago

doi: 10.11607/jomi.19suppl.intro

GROUP 1

Primary Stability and Osseointegration

Group Chair

John Brunski, PhD

Group Participants

Reva Barewal, DDS, MS

Lyndon F. Cooper, DDS, PhD

Hugo De Bruyn, DDS, PhD, Prof

Marco Degidi, DDS, MD

Bryan Frantz, DMD

Jill Helms, DDS, PhD

Stefan Horn

Stephen Jacobs, BDS, FDS RCPS, MJDF, RCS (Eng)

Peter Karsant, DDS

AnnaKarin Lundgren, DDS, PhD

Hans Malmstrom, DDS

Alan Meltzer, DMD, MScD

Alberto Monje, DDS

Michael Norton, BDS, FDS, RCS(Ed)

Thomas Oates, DMD, PhD

Edward Sevetz, DMD

Amerian Sones, DMD, MS

D. Rick Sumner, PhD

Hom-Lay Wang, DDS, MSD, PhD

Observers

Lars Janson, Southern Implants

Priya Menon, Dentsply Sirona

Thaddeus Picklo, Osstell


©2019 by Quintessence Publishing Co Inc.

Relationship Between Primary/Mechanical and Secondary/Biological Implant Stability

Alberto Monje, DDS, MS, PhD1,2/Andrea Ravidà, DDS, MS3/Hom-Lay Wang, DDS, MS, PhD3/ Jill A. Helms, DDS, PhD4/John B. Brunski, PhD4

Purpose: This systematic review was prepared as part of the Academy of Osseointegration (AO) 2018 Summit,

held August 8–10 in Oak Brook Hills, Illinois, to assess the relationship between the primary (mechanical)

and secondary (biological) implant stability. Materials and Methods: Electronic and manual searches were

conducted by two independent examiners in order to address the following issues. Meta-regression analyses

explored the relationship between primary stability, as measured by insertion torque (IT) and implant stability

quotient (ISQ), and secondary stability, by means of survival and peri-implant marginal bone loss (MBL).

Results: Overall, 37 articles were included for quantitative assessment. Of these, 17 reported on implant

stability using only resonance frequncy analysis (RFA), 11 used only IT data, 7 used a combination of RFA

and IT, and 2 used only the Periotest. The following findings were reached:

• Relationship between primary and secondary implant stability: Strong positive statistically significant

relationship (P < .001).

• Relationship between primary stability by means of ISQ and implant survival: No statistically significant

relationship (P = .4).

• Relationship between IT and implant survival: No statistically significant relationship (P = .2).

• Relationship between primary stability by means of ISQ unit and MBL: No statistically significant

relationship (P = .9).

• Relationship between IT and MBL: Positive statistically significant relationship (P = .02).

• Accuracy of methods and devices to assess implant stability: Insufficient data to address this issue.

Conclusion: Data suggest that primary/mechanical stability leads to more efficient achievement of

secondary/biological stability, but the achievement of high primary stability might be detrimental for bone

level stability. While current methods/devices for tracking implant stability over time can be clinically useful,

a robust connection between existing stability metrics with implant survival remains inconclusive. Int J Oral MaxIllOfac IMplants 2019;34(suppl):s7–s23. doi: 10.11607/jomi.19suppl.g1

Keywords: alveolar bone, bone homeostasis, dental implants, diagnostic, implant stability, mechanical, resonance frequency analysis

Accepting that osseointegration involves a dy-namic orchestration of de novo bone formation

and remodeling of pre-existing interfacial bone un-der implant function,1 the significance of achieving mechanical (“primary”) stability is imperative.2 Un-der ideal conditions, there will be a cascade of cel-lular and molecular phenomena—including blood clot formation, angiogenesis, osteoprogenitor cell migration, woven bone apposition in bone-implant gaps, secondary remodeling of the woven bone, and remodeling of pre-existing peri-implant bone—all of which play a role in the stabilization of the implant in its site.3,4 However, under non-ideal conditions, such

1Department of Oral Surgery and Stomatology, ZMK School of Dental Medicine, University of Bern, Switzerland.

2Department of Periodontology, Universidad Internacional de Catalunya, Barcelona, Spain.

3Department of Periodontology, University of Michigan, Ann Arbor, Michigan, USA.

4Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford University, Palo Alto, California, USA.

Correspondence to: Dr John Brunski, Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, 1651 Page Mill Drive, Palo Alto, CA 94305, USA. Email: [email protected]

s8 Volume 34, Supplement, 2019

Group 1

as conditions of excessive micromovements of the im-plant due to lack of mechanical stability, this cascade of events can be perturbed, resulting in fibrous encap-sulation, earlier described as fibroplasia,5 which in turn may lead to implant failure.

From a different perspective, the achievement of “high” mechanical stability—with the term “high” sig-nifying the use of a “larger-than-normal” insertion torque—involves the risk of causing a deleterious effect upon peri-implant tissue stability. For example, recent studies in animal models have focused on the inter-play between biology and mechanics in osseointegra-tion.6–12 Based on these findings, the insertion of dental implants under “high” torque triggers an increased spa-tial extent of interfacial microfractures and related bone resorption, which in turn can compromise osseointe-gration. On the other hand, implants placed with “low” insertion torque (ie, lower than typically used) showed significantly smaller compressive strains in peri-implant bone and minimal cell death, which, in turn, may blunt the oft-reported “dip” in plots of implant stability over time.13–15

In light of these issues surrounding stability and measurements thereof, a search for a quantitative met-ric that would enable clinicians to predict successful performance of dental implants—regardless of their placement and/or loading protocols—has represented an active thrust in dental research within the last two decades. For instance, it has resulted in the develop-ment of vibration tools (ie, resonance frequency analy-sis) or devices using impact to measure tooth stability (ie, Periotest).16 Along these lines, an operator’s clinical perception of insertion torque achieved during im-plant placement has also become a metric of stability.

In the contemporary era of implant dentistry, where protocols such as immediate placement and/or imme-diate loading are common, these protocols are carried out mainly based on pre- and intraoperative determi-nants, including judgments about bone characteristics (ie, trabecular density or proportion of cancellous and cortical bone) and implant stability.17,18 In addition, numerous bone-condensing approaches are gain-ing popularity among clinicians due to the actual or perceived enhancement in intraoperative stability or the perception thereof.19,20 Nonetheless, the fate of these techniques is still unknown, given that it is yet to be elucidated whether there is a threshold of in-sertion torque or implant stability quotient value be-yond which implant stability is compromised. Hence, the primary purpose of this systematic review was to shed light on the relationship of mechanical (primary) to secondary stability by means of quantitative data. Moreover, the nature, accuracy, and reliability of the devices to measure implant stability were analyzed as the secondary purpose.

MATERIALS AND METHODS

ProtocolThe protocol followed the PRISMA (Preferred Report-ing Items for Systematic Review and Meta-Analyses) statement.21 The review protocol was registered and allocated the identification number CRD42018094624 in the PROSPERO International Prospective Register of Systematic Reviews hosted by the National Institute for Health Research, University of York, Centre for Re-views and Dissemination.

Focused Questions1. What are the relationships between mechanical

(primary) stability—by means of implant stability quotient (ISQ) or insertion torque (IT)—and biological stability in humans—by means of implant stability quotient?

2. What are the relationships between implant mechanical (primary) stability—by means of ISQ and IT—and implant survival (≥ 12 months) and peri-implant bone stability?

3. What is the reliability/accuracy of the available quantitative tools to monitor implant stability?

PI(E)CO (Patient, Intervention [Exposure], Comparison, Outcome) Question 1• P: Partially or completely edentulous patients• I(E): Placement of dental implants to restore

function and/or esthetics with high ISQ• C: Placement of dental implants to restore function

and/or esthetics with low ISQ• O (Primary outcome): Biological stability (by means

of ISQ)• O (Secondary outcome): Implant survival (or implant

failure), peri-implant marginal bone stability (or loss)

PI(E)CO Question 2• P: Partially or completely edentulous patients• I(E): Placement of dental implants to restore function

and/or esthetics achieving primary stability• C: Placement of dental implants to restore function

and/or esthetics not achieving primary stability• O (Primary outcome): Biological stability (by means

of ISQ)• O (Secondary outcome): Implant survival (or implant

failure), peri-implant marginal bone stability (or loss)

PI(E)CO Question 3• P: Partially or completely edentulous patients• I(E): Placement of dental implants under high IT• C: Placement of dental implants under low (or no) IT• O (Primary outcome): Biological stability (by means

of ISQ)


Monje et al

• O (Secondary outcome): Implant survival (or implant failure), peri-implant marginal bone stability (or loss)

Case Definition for Primary/Mechanical and Secondary/Biological Stability• Primary/mechanical stability: The mechanical en-

gage ment (interlock) attained at the time of implant placement; it is governed by implant macro-design (diameter/length and thread design), implant drilling protocol, and bone macro-architecture.

• Secondary/biological stability: The biological en-gage ment and homeostasis by means of bone apposition to implant, which occurs after implant placement, influenced by factors including implant micro-design (surface characteristics) and bone macro- and micro-architecture plus implant loading.

Eligibility CriteriaInclusion Criteria• Randomized and nonrandomized prospective

or retrospective human case-control or cohort comparative studies evaluating implants reporting on the relationship between implant primary and secondary stability

• ≥ 12 months after implant placement• ≥ 10 participants • Quantitative implant stability value recorded by any

commercialized device/method

Exclusion Criteria• Studies in which primary and/or secondary stability

are not reported by means of a quantitative value• Studies in which the primary outcome is not

reported• Studies that assess the impact of bone grafting on

primary and/or secondary stability (ie, maxillary sinus floor elevation)

• < 12 months• < 10 participants

In addition, in order to gain more insight about the findings from human evidence, the screening of the electronic databases also tried to find preclinical and in vitro studies that could corroborate human findings at mechanobiological, cellular, and molecular levels.

Interventions/Methods to Assess Implant StabilityPrimary and secondary implant stability were assessed using the following tools and methods (Table 1):

• Traditional clinical methods: percussion, two instruments, radiograph, vibration analysis, Perio-test (damping effect), Ostell (resonance frequency analysis [RFA])

• Torque test: insertion torque, reverse torque

Information SourcesElectronic Search. Electronic databases were used as sources in the search for studies satisfying the inclusion criteria: (1) National Library of Medicine (MEDLINE via PubMed), (2) Cochrane Central Register of Controlled Trials, and (3) EMBASE database. These databases were searched for studies published until January 2018. The search was not filtered/limited by language.

Manual Search. All reference lists of the selected studies were checked for cross-references. The follow-ing journals were hand-searched from year 2016 to 2018: Journal of Clinical Periodontology, Journal of Periodontology, Clinical Oral Implants Research, The International Journal of Oral & Maxillofacial Implants, European Journal of Oral Implantology, Implant Dentistry, International Journal of Oral and Maxillofacial Surgery, Journal of Oral and Maxillofacial Surgery, and Clinical Implant Dentistry and Related Research.

Grey Literature Search. The System for Information on Grey Literature in Europe (SIGLE) database, through the “OpenGrey” www.opengrey.eu web page, was screened for potential data yet not published.

Table 1 Tools and Methods to Assess Implant Primary and Secondary Stability

Reliability Feasibility Major concern

Traditional clinical methods

Percussion Low Good No actual value

Two instruments Medium Good Low sensitivity

Radiograph Low Poor Low sensitivity

Vibration analysis

Periotest (damping effect) Low Low Not reliable

Resonance frequency analysis Medium Good Low specificity

Torque test

Insertion torque High Good One-time assessment

Reverse torque High Fair Too destructive

http://www.opengrey.eu


Group 1

Search StrategyA search strategy applying MeSH keywords when pos-sible and title/abstract keywords was carried out as follows: (((((((((((((((“jaw, edentulous”[MeSH Terms] OR “mouth, edentulous”[MeSH Terms]) OR “jaw, edentu-lous, partially”[MeSH Terms]) AND “dental implantation, endosseous”[MeSH Terms]) OR “dental implantation, endosseous”[MeSH Terms]) OR “dental implantation, endosseous”[MeSH Terms]) OR “dental implants”[MeSH Terms]) OR “dental implantation”[MeSH Terms]) AND primary stability[Title/Abstract]) OR mechani-cal stability[Title/Abstract]) OR high stability[Title/Abstract]) AND survival[Title/Abstract]) OR reso-nance frequency analysis[Title/Abstract]) OR insertion torque[Title/Abstract]) OR failure[Title/Abstract]) OR marginal bone loss[Title/Abstract]) OR crestal bone loss[Title/Abstract] AND (Clinical Trial[ptyp] AND “humans”[MeSH Terms]).

Due to the unsatisfactory/insufficient result yield-ed when using MeSH keywords on the proposed screening, another wider screening was carried out to ascertain accuracy in the screening of all poten-tial manuscripts. The secondary screening was typed as follows: (“dental implants”[Title/abstract] OR (“dental”[All Fields] AND “implants”[All Fields]) OR “den-tal implants”[All Fields] OR (“dental”[All Fields] AND “implant”[All Fields]) OR “dental implant”[All Fields]) AND (primary[All Fields] AND stability[All Fields]) OR (mechanical[All Fields] AND stability[All Fields]) OR “insertion”[All Fields]) AND (“torque”[MeSH Terms] OR “torque”[All Fields])) AND ((“biology”[MeSH Terms] OR “biology”[All Fields] OR “biological”[All Fields]) AND stability[All Fields]) OR (“osseointegration”[MeSH Terms] OR “osseointegration”[All Fields]) OR stability[All Fields] AND (implant[All Fields] AND failure[All Fields]) OR (implant[All Fields] AND success[All Fields]) OR (implant[All Fields] AND (“mortality”[Subheading] OR “mortality”[All Fields] OR “survival”[All Fields] OR “survival”[MeSH Terms])) OR (“resonance frequency analysis”[MeSH Terms] OR (“resonance”[All Fields] AND “frequency”[All Fields] AND “analysis”[All Fields]) OR “res-onance frequency analysis”[All Fields]) OR (implant[All Fields] AND stability[All Fields] AND quotient[All Fields]) OR (retrieval[All Fields] AND (“torque”[MeSH Terms] OR “torque”[All Fields])) OR (marginal[All Fields] AND (“bone diseases, metabolic”[MeSH Terms] OR (“bone”[All Fields] AND “diseases”[All Fields] AND “metabolic”[All Fields]) OR “metabolic bone diseases”[All Fields] OR (“bone”[All Fields] AND “loss”[All Fields]) OR “bone loss”[All Fields])) OR (crestal[All Fields] AND (“bone dis-eases, metabolic”[MeSH Terms] OR (“bone”[All Fields] AND “diseases”[All Fields] AND “metabolic”[All Fields]) OR “metabolic bone diseases”[All Fields] OR (“bone”[All Fields] AND “loss”[All Fields]) OR “bone loss”[All Fields])) AND (Clinical Trial[ptyp] AND “humans”[MeSH Terms]).

Screening MethodsThree reviewers (AM, AR, and HLW) did the primary search by independent screening of the titles and ab-stracts. The same reviewers selected for evaluation the full manuscript of those studies meeting the inclusion criteria, or those with insufficient data in the title and abstract to make a clear decision. Any disagreement was resolved by discussion with the rest of the authors. The Cohen’s kappa interexaminer agreement (percent-age of agreement and kappa correlation coefficient) of the screening method was calculated and reported.

Data ExtractionTwo reviewers (AM and AR) extracted the data. Authors of studies were contacted for clarification when data were incomplete or missing. If agreement could not be reached, data were excluded until further clarification emerged. When the results of a study were published more than once, the data with the longest follow-up were included only once.

Quality Assessment (Risk of Bias in Individual Studies)A quality assessment of the included randomized clinical trials (RCTs) and controlled clinical trials (CCTs) was performed according to the Cochrane Handbook for Systematic Reviews of Interventions22 and by the CONSORT statement.23 Six main quality criteria were as-sessed: sequence generation, allocation concealment, blinding treatment outcomes to outcome examiners, completeness of follow-up, selective outcome report-ing, and other sources of bias. These criteria were rated to be in low, unclear, or high risk of bias depending on the descriptions given for each individual field.

The Newcastle-Ottawa scale for cohort studies and a modification of the scale for cross-sectional studies were used for the assessment of risk of bias in individu-al observational studies.24 This scale includes five main categories: representativeness of the exposed cohort, ascertainment of exposure, assessment of outcome, follow-up long enough for the outcome of interest, and adequacy of follow-up.

Risk of Bias Across StudiesThe publication bias was evaluated using a funnel plot and the Egger´s linear regression method. A sen-sitivity analysis of the meta-analysis results was also performed.

RESULTS

Study SelectionA total of 2,292 records were identified through the electronic search after removal of duplicates; they


Monje et al

were supplemented with 25 citations from the manual search and 4 citations through screening bibliogra-phies of relevant included/excluded articles, as illus-trated in Fig 1.

Upon exclusion of reports deemed ineligible, this left 2,247 titles and abstracts, and 65 studies remained for full-text evaluation. Finally, 28 studies were exclud-ed for not meeting the inclusion criteria and one fur-ther study could not be included as it did not report the exact values, but rather reported ranges,25 leaving 37 studies26–62 eligible for inclusion in the qualitative and quantitative analyses (Fig 1; Tables 2 to 4).

Of these, 17 reported implant stability only us-ing RFA,28,29,31,32,36,38,40,46–48,50,52,54,55,58,60,62 11 only IT,33–35,39,42,44,45,51,53,56,59 7 a combination of RFA and IT,27,30,41,43,49,57,61 and 2 the Periotest.26,37 Of the 36 studies included in the qualitative and quan-titative evaluation, 22 were prospective cohort (PC) studies,27–29,32–35,39,42,45–52,54,57,60–62 1 was a retrospective cohort (RC) study,37 and 14 were RCTs.30,31,36,38,40,41,43,44,53,55,56,58–60

• For the evaluation of RFA, 24 studies comprised a total of 892 participants. The participants had a total of 2,137 implants that were assessed.

• For the evaluation of IT, 18 studies comprised a total of 1,387 participants. The participants had a total of 2,646 implants that were assessed.

• For the evaluation of Periotest, 2 studies comprised a total of 56 participants. The participants had a total of 128 implants that were assessed.

Relationship Between Primary and Secondary Implant StabilityThe study demonstrated a strong positive statisti-cal significance between primary and secondary stability (P < .001) with a coefficient of 0.847 when using the RFA tool. In other words, roughly, there is 85% of variation from primary to secondary stabili-ty. No relationship between primary and secondary stability could be evaluated using the other meth-ods (Fig 2).

Primary Outcome on Implant SurvivalThe inter-study heterogeneity reached 99.9% of the total variability (I2 = 0.999; P < .001). Due to this issue, two studies were excluded from the analysis (Atieh et al28; Kronstrom et al41). The mean implant survival rate was 98.4% (95% CI: 97.3%–99.3%).

Records identified through the MEDLINE database searching

(n = 2,292)

Additional records identified through other sources

(n = 4)

Records excluded (n = 2,247)

Full-text articles excluded (n = 29)

Studies included in quantitative assessment on RFA

(n = 24*)

Studies included in quantitative assessment on Periotest

(n = 2)

Incl

uded

Elig

ibili

tyId

entifi

catio

nScr

eeni

ng

Records identified through other electronic sources

(n = 25)

Records after duplicates removed (n = 2,321)

Studies included in qualitative synthesis (n = 37)

Full-text articles assessed for eligibility (n = 65)

Records screened (n = 74)

Studies included in quantitative assessment on IT

(n = 18*)

Fig 1 PRISMA Flowchart of the screening process. *Seven trials evaluated and reported implant stability with resonance frequency analysis (RFA) and insertion torque (IT).


Group 1

Table 2 Studies Included in the Systematic Review Using Resonance Frequency Analysis (RFA) to Assess Primary and Secondary Implant Stability

Study (year) Study design Follow-upParticipants

(n)Implants

(n)

Primary stability Secondary stability Confounders

Implant survival

(%)

Implant failure

(%)Success rate (%)

MBL follow-up

(mm)Method IT Value Method Value TimingSmoking

(%)Plaque index

(%)Bleeding index (%)

Keratinized mucosa (mm)

Alghamdi et al27 (2011) PC 12 mo from IP 29 26 RFA 35.19 ± 4.79 68.5 ± 4.81 NA NA NA 0 15.42 ± 3.04 11.52 ± 2.03 NA 100 0 100 NA

26 34.62 ± 5.82 66.6 ± 5.41 100 NA 100 0 100 NA

Atieh et al28 (2014) PC 12 mo from IP/PL 28 28 RFA NA 79.7 ± 4.4 RFA 73.4 ± 12.4 8 wk 0 NA NA NA 78.6 21.4 NA NA

Balleri et al29 (2002) PC 12 mo from PL 14 45 RFA NA NA RFA 69 ± 6.5 12 mo NA NA NA NA 100 0 NA 0.3 ± 0.3

Barewal et al30 (2012) RCT 36 mo from IP 38 8 RFA 10.01 ± 45.81 58 ± 5.5 NA NA NA 0 NA NA NA 87.5 12.5 NA 0.22

13 32.28 ± 11.04 72 ± 3.1 100 0

19 16.61 ± 7.78 70 ± 4.2 100 0

Bechara et al31 (2017) RCT 36 mo from IP 33 45 RFA NA 68.2 RFA 71.6 3 y 21 NA NA NA 100 0 NA 0.89 ± 0.25

Becker et al32 (2013) PC 24 mo from IP 76 100 RFA NA 72.1 RFA 72.6 24 mo NA NA NA NA 93 7 NA 0.6

Hof et al36 (2014) RCT 12 mo from IP 21 42 RFA NA 75.5 RFA 80.5 12 mo NA NA 10 NA 100 0 NA 0.69

42 NA 78.5 81.3 12 mo 97.6 2.4 0.68

Karabuda et al38 (2011) RCT 12 mo from PL 22 48 RFA NA 55.46 ± 8.29 RFA 58.15 ± 6.52 6 wk NA NA NA NA 100 0 NA 0.46 ± 0.07

48 NA 56.63 ± 8.19 58.21 ± 5.2 97.1 2.9 NA 0.43 ± 0.11

Kim et al40 (2015) RCT 12 mo from PL 21 22 RFA NA 66.8 ± 7.4 NA NA NA NA NA NA NA 84 16 NA NA

12 mo from PL 24 NA 66.2 ± 6.5 NA NA NA N NA NA 100 0 NA NA

Kronstrom et al41 (2010) RCT 12 mo from IP/PL 36 3 IT 20 74.1 RFA 81.7 12 mo NA NA NA NA 100 0 NA 0.44 ± 0.4

42 30 81 19

1 35 100 0

9 40 78 22

Malchiodi et al43 (2016) RCT 12 mo from IP 40 40 RFA 49.0 ± 9.95 63.95 ± 8.81 RFA 67.48 ± 5.95 3 mo 25 NA NA NA 100 0 100 0.54 ± 0.38

Norton62 (2017) PC 12 mo from IP 22 3 RFA < 5 66.83 RFA 84.83 3 mo 4.5 NA NA NA 100 0 NA 0.07

9 5–10 63.22 78.22

18 > 10 67.11 79.30

Olsson et al46 (2003) PC 12 mo from PL 10 61 RFA NA 60.1 ± 3.6 RFA 62.8 ± 1.6 4 mo NA NA NA NA 93.4 6.6 N/A 1.3 ± 0.6

Östman et al47 (2005) PC 12 mo from PL 40 123 RFA NA 62.9 ± 4.9 RFA 64.5 ± 4.8 6 mo 5 NA NA NA 99.6 0.4 NA 0.78 ± 0.90

120 NA 61.3 ± 8.8 62.6 ± 7 100 0 0.91 ± 1.04

Östman et al49 (2013) PC 12 mo from IP 21 139 RFA 53.1 73.1 ± 6.3 NA NA NA NA NA NA NA 99.4 0.6 NA 1.01 ± 0.85

Östman et al48 (2008) PC 12 mo from PL 77 257 RFA NA 72.2 ± 7.5 RFA 72.5 ± 5.7 6 mo NA NA NA NA 98.4 1.6 82.9 0.7 ± 0.78

Pieri et al50 (2012) PC 24 mo from PL 25 61 RFA NA 67.35 ± 6.67 RFA 72.91 ± 5.07 24 mo 16 NA 13.6 < 2 mm (2 implants)

96.8 3.2 96.8 0.6 ± 0.13

Rao and Benzi52 (2007) PC 12 mo from PL 46 51 RFA NA 71.9 RFA 74.1 12 mo NA NA NA NA 100 0 NA 1.12 ± 1.06

Sennerby et al54 (2012) PC 12 mo from PL 90 218 RFA NA 73.7 ± 7.6 RFA 76.7 ± 5.2 12 mo NA NA NA NA 98.6 2.4 NA 0.6 ± 0.8

Shayesteh et al55 (2013) RCT 12 mo from IP 30 23 RFA NA 70.9 RFA 72.71 3 mo 0 NA NA NA 100 0 NA 0.41 ± 0.22

23 NA 64.7 71.37 100 0 0.34 ± 0.21

Tatli et al57 (2014) PC 12 mo from PL 23 77 RFA 36.8 ± 3.8 73.6 ± 5.8 RFA 74.8 ± 5.6 3 mo NA NA NA NA 100 0 NA NA

Turkyilmaz et al58 (2008) RCT 12 mo from IP 20 20 RFA NA 76.2 ± 2.8 RFA 76.4 ± 2.5 12 mo NA NA NA NA 100 0 NA 1 ± 0.3

RCT 12 mo from IP 20 NA 75.6 ± 4.5 RFA 76.4 ± 2.8 12 mo NA NA NA NA 100 0 NA 0.9 ± 0.3

Zix et al60 (2005) PC 12 mo from IP 35 120 RFA NA 52.5 ± 7.9 NA NA NA 22.8 NA NA NA NA NA NA 0.7 ± 1.1

Zwaan et al61 (2016) PC 12 mo from PL 97 163 RFA 41.3 ± 12.0 73.7 ± 6.4 RFA 75.0 ± 4.5 6 mo 24.5 NA NA NA 96.9 3.1 NA 0.5 ± 0.4

PC = prospective cohort study; RCT = randomized clinical trial; IT = insertion torque; IP = implant placement; PL = postloading.


Monje et al

Table 2 Studies Included in the Systematic Review Using Resonance Frequency Analysis (RFA) to Assess Primary and Secondary Implant Stability

Study (year) Study design Follow-upParticipants

(n)Implants

(n)


Implant survival

(%)

Implant failure

(%)Success rate (%)

MBL follow-up

(mm)Method IT Value Method Value TimingSmoking

(%)Plaque index

(%)Bleeding index (%)


Alghamdi et al27 (2011) PC 12 mo from IP 29 26 RFA 35.19 ± 4.79 68.5 ± 4.81 NA NA NA 0 15.42 ± 3.04 11.52 ± 2.03 NA 100 0 100 NA

26 34.62 ± 5.82 66.6 ± 5.41 100 NA 100 0 100 NA

Atieh et al28 (2014) PC 12 mo from IP/PL 28 28 RFA NA 79.7 ± 4.4 RFA 73.4 ± 12.4 8 wk 0 NA NA NA 78.6 21.4 NA NA

Balleri et al29 (2002) PC 12 mo from PL 14 45 RFA NA NA RFA 69 ± 6.5 12 mo NA NA NA NA 100 0 NA 0.3 ± 0.3

Barewal et al30 (2012) RCT 36 mo from IP 38 8 RFA 10.01 ± 45.81 58 ± 5.5 NA NA NA 0 NA NA NA 87.5 12.5 NA 0.22

13 32.28 ± 11.04 72 ± 3.1 100 0

19 16.61 ± 7.78 70 ± 4.2 100 0

Bechara et al31 (2017) RCT 36 mo from IP 33 45 RFA NA 68.2 RFA 71.6 3 y 21 NA NA NA 100 0 NA 0.89 ± 0.25

Becker et al32 (2013) PC 24 mo from IP 76 100 RFA NA 72.1 RFA 72.6 24 mo NA NA NA NA 93 7 NA 0.6

Hof et al36 (2014) RCT 12 mo from IP 21 42 RFA NA 75.5 RFA 80.5 12 mo NA NA 10 NA 100 0 NA 0.69

42 NA 78.5 81.3 12 mo 97.6 2.4 0.68

Karabuda et al38 (2011) RCT 12 mo from PL 22 48 RFA NA 55.46 ± 8.29 RFA 58.15 ± 6.52 6 wk NA NA NA NA 100 0 NA 0.46 ± 0.07

48 NA 56.63 ± 8.19 58.21 ± 5.2 97.1 2.9 NA 0.43 ± 0.11

Kim et al40 (2015) RCT 12 mo from PL 21 22 RFA NA 66.8 ± 7.4 NA NA NA NA NA NA NA 84 16 NA NA

12 mo from PL 24 NA 66.2 ± 6.5 NA NA NA N NA NA 100 0 NA NA

Kronstrom et al41 (2010) RCT 12 mo from IP/PL 36 3 IT 20 74.1 RFA 81.7 12 mo NA NA NA NA 100 0 NA 0.44 ± 0.4

42 30 81 19

1 35 100 0

9 40 78 22

Malchiodi et al43 (2016) RCT 12 mo from IP 40 40 RFA 49.0 ± 9.95 63.95 ± 8.81 RFA 67.48 ± 5.95 3 mo 25 NA NA NA 100 0 100 0.54 ± 0.38

Norton62 (2017) PC 12 mo from IP 22 3 RFA < 5 66.83 RFA 84.83 3 mo 4.5 NA NA NA 100 0 NA 0.07

9 5–10 63.22 78.22

18 > 10 67.11 79.30

Olsson et al46 (2003) PC 12 mo from PL 10 61 RFA NA 60.1 ± 3.6 RFA 62.8 ± 1.6 4 mo NA NA NA NA 93.4 6.6 N/A 1.3 ± 0.6

Östman et al47 (2005) PC 12 mo from PL 40 123 RFA NA 62.9 ± 4.9 RFA 64.5 ± 4.8 6 mo 5 NA NA NA 99.6 0.4 NA 0.78 ± 0.90

120 NA 61.3 ± 8.8 62.6 ± 7 100 0 0.91 ± 1.04

Östman et al49 (2013) PC 12 mo from IP 21 139 RFA 53.1 73.1 ± 6.3 NA NA NA NA NA NA NA 99.4 0.6 NA 1.01 ± 0.85

Östman et al48 (2008) PC 12 mo from PL 77 257 RFA NA 72.2 ± 7.5 RFA 72.5 ± 5.7 6 mo NA NA NA NA 98.4 1.6 82.9 0.7 ± 0.78

Pieri et al50 (2012) PC 24 mo from PL 25 61 RFA NA 67.35 ± 6.67 RFA 72.91 ± 5.07 24 mo 16 NA 13.6 < 2 mm (2 implants)

96.8 3.2 96.8 0.6 ± 0.13

Rao and Benzi52 (2007) PC 12 mo from PL 46 51 RFA NA 71.9 RFA 74.1 12 mo NA NA NA NA 100 0 NA 1.12 ± 1.06

Sennerby et al54 (2012) PC 12 mo from PL 90 218 RFA NA 73.7 ± 7.6 RFA 76.7 ± 5.2 12 mo NA NA NA NA 98.6 2.4 NA 0.6 ± 0.8

Shayesteh et al55 (2013) RCT 12 mo from IP 30 23 RFA NA 70.9 RFA 72.71 3 mo 0 NA NA NA 100 0 NA 0.41 ± 0.22

23 NA 64.7 71.37 100 0 0.34 ± 0.21

Tatli et al57 (2014) PC 12 mo from PL 23 77 RFA 36.8 ± 3.8 73.6 ± 5.8 RFA 74.8 ± 5.6 3 mo NA NA NA NA 100 0 NA NA

Turkyilmaz et al58 (2008) RCT 12 mo from IP 20 20 RFA NA 76.2 ± 2.8 RFA 76.4 ± 2.5 12 mo NA NA NA NA 100 0 NA 1 ± 0.3

RCT 12 mo from IP 20 NA 75.6 ± 4.5 RFA 76.4 ± 2.8 12 mo NA NA NA NA 100 0 NA 0.9 ± 0.3

Zix et al60 (2005) PC 12 mo from IP 35 120 RFA NA 52.5 ± 7.9 NA NA NA 22.8 NA NA NA NA NA NA 0.7 ± 1.1

Zwaan et al61 (2016) PC 12 mo from PL 97 163 RFA 41.3 ± 12.0 73.7 ± 6.4 RFA 75.0 ± 4.5 6 mo 24.5 NA NA NA 96.9 3.1 NA 0.5 ± 0.4

PC = prospective cohort study; RCT = randomized clinical trial; IT = insertion torque; IP = implant placement; PL = postloading.


Group 1

Table 3 Studies Included in the Systematic Review Using Insertion Torque to Determine Primary Stability

Study (year)Study design Follow-up

Participants (n)

Implants (n)

Primary stability Confounders

Implant failure (%)

Success rate %

MBL follow-up (mm)Method

Value (Ncm)

Smoking (%)

Plaque index (%)

Bleeding index (%)


Implant survival (%)

Alghamdi et al27 (2011) PC 12 mo from IP 29 26 IT 35.19 ± 4.79 0 15.42 ± 3.04 11.52 ± 2.03 NA 100 0 100 NA26 34.62 ± 5.82

Barewal et al30 (2012) RCT 36 mo from IP 38 8 IT 10.01 ± 4.58 0 NA NA NA 87.5 12.5 NA 0.2213 32.28 ± 11.04 100 019 16.61 ± 7.78 100 0

Vanden Bogaerde et al59 (2016) RCT 36 mo from IP 11 11 IT 38 ± 11.4 27.2 NA NA NA 100 0 NA 0.4 ± 0.736 mo from IP 11 39.5 ± 9.1 NA NA NA 90.91 9.09 NA 0.6 ± 0.5

Calandriello et al33 (2003) PC 12 mo from IP 26 20 IT 66.3 ± 8.4 NA NA NA NA 98 2 0 1.22 ± 0.819 60.9 ± 1111 51.3 ± 25.4

Degidi et al34 (2012) PC 12 mo from IP 13 51 IT 12.6 ± 3.6 NA NA NA NA 98 2 NA 0.6 ± 131 35.4 ± 7.3 100 0 0.5 ± 0.8

Grandi et al35 (2013) PC 12 mo from IP 102 114 IT 74.8 ± 7.9 22.3 NA NA NA 100 0 NA 0.41 ± 0.1842 37.4 ± 8.2 23.03 NA NA NA 100 0 0.45 ± 0.25

Khayat et al39 (2013) PC 12 mo from IP 6 9 IT 37.1 NA NA NA NA 100 0 NA 1.09 ± 0.6232 42 110.6 NA NA NA NA 100 0 1.24 ± 0.75

Kronstrom et al41 (2010) RCT 12 mo from IP/PL 36 3 IT 20 NA NA NA NA 100 0 NA NA42 30 81 191 35 100 09 40 78 22

Maiorana et al42 (2015) PC 46 mo from IP 189 377 IT 45 NA NA NA NA 99.7 0.3 NA NAMalchiodi et al43 (2016) RCT 12 mo from IP 40 40 IT 49.0 ± 9.95 25 NA NA NA 100 0 100 0.54 ± 0.38Marconcini et al44 (2018) RCT 36 mo from IP 116 58 IT 29.1 ± 6.6 30.1 NA NA NA 98.2 1.8 NA 0.96 ± 0.46

58 70.6 ± 8.5 91.3 8.7 1.16 ± 0.61Norton45 (2011) PC 46 mo from IP 61 68 IT 22.5 8.19 NA NA NA 95.5 4.5 NA NANorton62 (2017) PC 12 mo from PL 30 22 IT < 20 NA NA NA NA 96.7 3.3 NA NAÖstman et al49 (2013) PC 12 mo from IP 42 139 IT 53.1 NA NA NA NA 99.4 0.6 NA 1.01 ± 0.85Rabel et al51 (2007) PC 12 mo from IP 263 408 IT 28.8 ± 15.2 NA NA NA NA 98.5 1.5 NA NA

194 25.9 ± 14.7 99.5 0.5Rizkallah et al53 (2013) RCT 15 mo from IP 145 390 IT 72 NA NA NA NA 97.7 2.3 NA NAStanford et al56 (2016) RCT 12 mo from PL 120 79 IT 31 ± 13 14.1 NA NA NA 94.9 5.1 NA 0.51 ± 0.72

87 22 ± 9 98.9 1.1 0.34 ± 0.49Tatli et al57 (2014) PC 12 mo from PL 23 77 IT 36.8 ± 3.8 NA NA NA NA 100 0 NA NAZwaan et al61 (2016) PC 12 mo from PL 97 163 IT 41.3 ± 12.0 24.5 NA NA NA 96.9 3.1 NA 0.5 ± 0.4

PC = prospective cohort study; RCT = randomized clinical trial; IP = implant placement; PL = postloading.

Table 4 Studies Included in the Systematic Review Using Periotest to Assess Primary and Secondary Implant Stability


Participants (n)

Implants (n)


Method Value Method Value TimingSmoking

(%)Plaque

index (%)Bleeding index (%)


Implant survival

(%)

Implant failure

(%)Success rate (%)

MBL follow-up

(mm)

Al-Hashedi et al26 (2016) RCT 12 mo from PL 20 20 Periotest –1.61 ± 2.02 Periotest –2.19 ± 2.23 12 mo from PL

0 NA NA NA 100 0 NA NA

20 Periotest 2.15 ± 2.52 Periotest 1.09 ± 2.83 12 mo from PL


Jeong et al37 (2015) RC 5 y from PL 36 88 Periotest –1.02 Periotest –0.74 5 y from PL

NA NA N/A NA 98.9 1.1 NA NA

RCT = randomized clinical trial; RC = retrospective cohort study; PL = postloading.


Monje et al

Table 3 Studies Included in the Systematic Review Using Insertion Torque to Determine Primary Stability


Participants (n)

Implants (n)

Primary stability Confounders

Implant failure (%)

Success rate %

MBL follow-up (mm)Method

Value (Ncm)

Smoking (%)

Plaque index (%)

Bleeding index (%)


Implant survival (%)

Alghamdi et al27 (2011) PC 12 mo from IP 29 26 IT 35.19 ± 4.79 0 15.42 ± 3.04 11.52 ± 2.03 NA 100 0 100 NA26 34.62 ± 5.82

Barewal et al30 (2012) RCT 36 mo from IP 38 8 IT 10.01 ± 4.58 0 NA NA NA 87.5 12.5 NA 0.2213 32.28 ± 11.04 100 019 16.61 ± 7.78 100 0

Vanden Bogaerde et al59 (2016) RCT 36 mo from IP 11 11 IT 38 ± 11.4 27.2 NA NA NA 100 0 NA 0.4 ± 0.736 mo from IP 11 39.5 ± 9.1 NA NA NA 90.91 9.09 NA 0.6 ± 0.5

Calandriello et al33 (2003) PC 12 mo from IP 26 20 IT 66.3 ± 8.4 NA NA NA NA 98 2 0 1.22 ± 0.819 60.9 ± 1111 51.3 ± 25.4

Degidi et al34 (2012) PC 12 mo from IP 13 51 IT 12.6 ± 3.6 NA NA NA NA 98 2 NA 0.6 ± 131 35.4 ± 7.3 100 0 0.5 ± 0.8

Grandi et al35 (2013) PC 12 mo from IP 102 114 IT 74.8 ± 7.9 22.3 NA NA NA 100 0 NA 0.41 ± 0.1842 37.4 ± 8.2 23.03 NA NA NA 100 0 0.45 ± 0.25

Khayat et al39 (2013) PC 12 mo from IP 6 9 IT 37.1 NA NA NA NA 100 0 NA 1.09 ± 0.6232 42 110.6 NA NA NA NA 100 0 1.24 ± 0.75

Kronstrom et al41 (2010) RCT 12 mo from IP/PL 36 3 IT 20 NA NA NA NA 100 0 NA NA42 30 81 191 35 100 09 40 78 22

Maiorana et al42 (2015) PC 46 mo from IP 189 377 IT 45 NA NA NA NA 99.7 0.3 NA NAMalchiodi et al43 (2016) RCT 12 mo from IP 40 40 IT 49.0 ± 9.95 25 NA NA NA 100 0 100 0.54 ± 0.38Marconcini et al44 (2018) RCT 36 mo from IP 116 58 IT 29.1 ± 6.6 30.1 NA NA NA 98.2 1.8 NA 0.96 ± 0.46

58 70.6 ± 8.5 91.3 8.7 1.16 ± 0.61Norton45 (2011) PC 46 mo from IP 61 68 IT 22.5 8.19 NA NA NA 95.5 4.5 NA NANorton62 (2017) PC 12 mo from PL 30 22 IT < 20 NA NA NA NA 96.7 3.3 NA NAÖstman et al49 (2013) PC 12 mo from IP 42 139 IT 53.1 NA NA NA NA 99.4 0.6 NA 1.01 ± 0.85Rabel et al51 (2007) PC 12 mo from IP 263 408 IT 28.8 ± 15.2 NA NA NA NA 98.5 1.5 NA NA

194 25.9 ± 14.7 99.5 0.5Rizkallah et al53 (2013) RCT 15 mo from IP 145 390 IT 72 NA NA NA NA 97.7 2.3 NA NAStanford et al56 (2016) RCT 12 mo from PL 120 79 IT 31 ± 13 14.1 NA NA NA 94.9 5.1 NA 0.51 ± 0.72

87 22 ± 9 98.9 1.1 0.34 ± 0.49Tatli et al57 (2014) PC 12 mo from PL 23 77 IT 36.8 ± 3.8 NA NA NA NA 100 0 NA NAZwaan et al61 (2016) PC 12 mo from PL 97 163 IT 41.3 ± 12.0 24.5 NA NA NA 96.9 3.1 NA 0.5 ± 0.4

PC = prospective cohort study; RCT = randomized clinical trial; IP = implant placement; PL = postloading.

Table 4 Studies Included in the Systematic Review Using Periotest to Assess Primary and Secondary Implant Stability


Participants (n)

Implants (n)


Method Value Method Value TimingSmoking

(%)Plaque

index (%)Bleeding index (%)


Implant survival

(%)

Implant failure

(%)Success rate (%)

MBL follow-up

(mm)

Al-Hashedi et al26 (2016) RCT 12 mo from PL 20 20 Periotest –1.61 ± 2.02 Periotest –2.19 ± 2.23 12 mo from PL


20 Periotest 2.15 ± 2.52 Periotest 1.09 ± 2.83 12 mo from PL


Jeong et al37 (2015) RC 5 y from PL 36 88 Periotest –1.02 Periotest –0.74 5 y from PL

NA NA N/A NA 98.9 1.1 NA NA

RCT = randomized clinical trial; RC = retrospective cohort study; PL = postloading.


Group 1

Relationship Between Primary Stability by Means of ISQ Unit and Implant SurvivalA statistically significant relationship was not yielded between mechanical stability determined by ISQ and implant survival (P = .511) (Figs 3 and 4).

Relationship Between IT and Implant SurvivalNo statistically significant relationship was reached be-tween the IT and implant survival (P = .227) (Fig 5).

Secondary Outcome on Marginal Bone LossThe inter-study heterogeneity reached 98.6% of the total variability (I2 = 0.986; P < .001). It was found that one study (Rao and Benzi52) accounted for the highest heterogeneity. Mean marginal bone loss was estimat-ed to be 0.69 mm (95% CI: 0.56–0.84 mm). (See Supple-mentary Figs S1 and S2, in online version of article at www.quintpub.com.)

60 65 70 75 80Primary stability

75

70

65

60

Sec

onda

ry s

tabi

lity

Author (year) Mean [95% CI]

Alghamdi et al (2011) 0.999 [0.998, 1.000]Balleri et al (2002) 0.999 [0.998, 1.000]Barewal et al (2012) 0.975 [0.967, 0.983]Bechara et al (2017) 0.999 [0.998, 1.000]Becker et al (2013) 0.930 [0.925, 0.935]Farzad et al (2004) 0.990 [0.988, 0.992]Hof et al (2014) 0.988 [0.985, 0.991]Karabuda et al (2011) 0.985 [0.983, 0.987] Kim et al (2015) 0.923 [0.912, 0.934]Malchiodi et al (2016) 0.999 [0.997, 1.001]Norton (2017) 0.999 [0.997, 1.001]Olsson et al (2003) 0.934 [0.926, 0.942]Östman et al (2005) 0.998 [0.998, 0.998]Östman et al (2013) 0.994 [0.993, 0.995]Östman et al (2008) 0.984 [0.983, 0.985]Pieri et al (2012) 0.968 [0.962, 0.974]Rao and Benzi (2007) 0.999 [0.998, 1.000]Sennerby et al (2012) 0.986 [0.985, 0.987]Shayesteh et al (2013) 0.999 [0.998, 1.000]Tatli et al (2014) 0.999 [0.998, 1.000]Turkyilmaz et al (2008) 0.999 [0.997, 1.001]Zwaan et al (2016) 0.969 [0.967, 0.971]RE Model 0.983 [0.973, 0.993]

0.900 0.940 0.980 1.020Observed outcome

Fig 2 Relationship between primary and sec-ondary stability.

Fig 3 (Right) Forest plot of relationship between primary stability by means of ISQ unit and implant survival.

60 65 70 75Primary stability

100

98

96

94

92

Sur

viva

l rat

e (%

)


100

99

98

97

96

95

Sur

viva

l rat

e (%

)

Fig 4 Relationship between primary stability by means of ISQ unit and implant survival.

Fig 5 Relationship between implant torque and implant survival.


Monje et al

Relationship Between Primary Stability by Means of ISQ Unit and Marginal Bone LossNo relationship could be established between the ISQ unit and marginal bone loss based on the existing evi-dence (P = .970) (Fig 6).

Relationship Between IT and Marginal Bone LossA statistically significant relationship between IT and marginal bone loss was yielded (0.027), favoring lower IT values. Each increase of 1 IT unit stands for a mar-ginal bone loss of 0.01 mm. The coefficient of determi-nation (R2) was found to be low (30.8%) (Fig 7).

Relationship Between Periotest Values and Implant SurvivalDue to the limited data of implant primary stability us-ing the Periotest, and the high level of heterogeneity (98%), no clear results could be obtained of this rela-tionship. The mean implant survival rate was 99.4% (95% CI: 98.4%–100%).

Risk of BiasRisk of bias is presented in Supplementary Tables S1 and S2 in the online version of this article at www. quintpub.com.

DISCUSSION

Main FindingsFindings of the present systematic review illustrated the role that mechanical stability in vivo plays on the achievement of biological stability. Moreover, it shed light on the detrimental effect that high implant IT may have upon peri-implant bone stability. Interest-ingly, the meta-regression analysis failed to identify

a statistically significant relationship between the implant mechanical (primary) stability—as recorded using ISQ units or, instead, by the IT value—and the survival rate. It can be suggested that this controver-sial finding might be due to the inaccuracy of the cur-rently used quantitative methods to evaluate implant stability in vivo.

Clinical ImplicationsGiven the fact that mechanical stability is advised to successfully achieve biological stability, it is recom-mended to tailor implant macro-design and drilling protocol according to the bone characteristics. In this sense, advances in personalized medicine (precision medicine) offers the potential to condition the pa-tient to achieve successful mechanical and biological stability by modifying bone biologic characteristics. Nevertheless, it should be kept in mind that the high IT values may jeopardize peri-implant marginal bone sta-bility. Moreover, it is worth noting that the suggested degree of mechanical stability sought by the clinician should take into account the implant placement and intended loading protocol.

In addition, RFA has shown to be useful in monitor-ing the dynamic transition (time course) from mechani-cal to biological stability. Hence, it is advisable to use RFA as a reference tool in monitoring to evaluate the short-term changes from baseline; that is, clinical de-cisions should not be based solely on an isolated ISQ value without considering any initial measurement. Along these lines, it is worth noting that this device was not developed to guarantee long-term stability, since after the achievement of biological stability, a myriad of confounding factors might be associated with im-plant failure, such as changes in prosthetic design or occlusal habits.

6055 65 70 75Primary stability

1.2

1.0

0.8

0.6

0.4Mar

gina

l bon

e lo

ss (

mm

)


1.2

1.0

0.8

0.6

0.4

Mar

gina

l bon

e lo

ss (

mm

)

Fig 6 Relationship between primary stability by means of ISQ unit and marginal bone loss.

Fig 7 Relationship between insertion torque and marginal bone loss.


Group 1

Are Our Findings Plausible? Biological and Biomechanical Mechanisms Underlying Our FindingsOn the Relationship of Primary and Secondary Sta-bility. The dynamic process of osseointegration is initi-ated at the moment the implant is inserted within the alveolar bone. The prerequisite of mechanical stability has been a matter of debate, in particular the thresh-old of stability that assures adequate biologic anchor-age. Given our understanding that the peri-implant healing in an aseptic environment arises from an or-chestration of cellular events triggered by the dam-age of the pre-existing substratum and ending with new bone formation and remodeling at the implant surface, it is logical to consider that any physical dis-ruption such as micromovement (above some thresh-old) might compromise this process. This was clearly depicted in a canine preclinical study where, after a healing period of 9 months, none of the non-stable implants reached osseointegration, with a layer of con-nective tissue interposed between the implant and the newly formed bone.2 Such findings confirmed earlier clinical observations.63,64 The present meta-regression analysis on clinical trials assessing the influence of pri-mary over secondary stability is consistent with the di-rect relationship between these two. Hence, in order to strengthen the odds of the achievement of osseo-integration, mechanical (primary) stability must be a first step. However, this begs the as-yet incompletely answered question of what exactly is the threshold value of implant micromotion—as measured by some appropriate method—that will interfere with proper osseointegration.

On the Relationship Between IT and Primary and Secondary Stability. Classically, it has been thought that a higher IT—ie, “higher than the standard recom-mended value”—might lead more predictably to os-seointegration. In light of this perspective, techniques that attempt to improve osteotomy viability and fit to the implant, eg, modified specialized cutting tools or the use of osteotomes, have been proposed. Neverthe-less, while it would intuitively make sense that increas-ing the interfacial bone volume and bone-implant contact could provide better primary mechanical sup-port to the implant, it is also known that condensing cancellous bone using an osteotome will result in sig-nificant damage to the bone, which in turn can lower the bone’s modulus and strength, thus weakening the bone-implant interface.65–68

It should also be noted that, besides implant macro-design (ie, governed by factors such as diam-eter, length, thread pattern, and cutting design), IT is directly related to bone characteristics. While it is true that the anatomic location is not synonymous with bone quality, nonetheless, type I bone predominates

in the anterior mandible while types III and IV are more frequently found in the posterior maxilla. Hence, gen-erally speaking, if all other factors are equal, IT is as-sumed to be higher in the mandible compared to the maxilla, with greater bone-implant contact (BIC) in the former than the latter. Now the question that remains unanswered is the following: If one tries to achieve a higher IT— and thus supposedly a higher stability—by increasing the BIC by employing an undersized os-teotomy, will this approach guarantee the process of osseointegration? The short answer is: no. At a cellular level, higher IT (> 50 Ncm), owing to undersized drill-ing or the insertion in compact bone, has been dem-onstrated to develop microfractures that may lead to tissue breakdown or even to an erratic process of os-seointegration.6 On the other side, although lower IT may lead to lesser primary stability, it does not affect the process of osseointegration as long as the healing is not disrupted by undue movement/load.

On the Relationship Between IT and Clinical Peri-implant Bone Stability. Many investigations have fo-cused on the identification of the “ideal” IT that would guarantee peri-implant bone stability. As noted previ-ously, this begs the question of the exact meaning of “low” vs “high” IT—a question that is only answered in the context of each paper dealing with that topic. Early findings suggested that low IT at implant placement might be associated with future fibrous encapsulation of the implant, as a result of the occurrence of implant micromotion.69 This idea has been a matter of debate, since osseointegration has been demonstrated in ani-mal models when an IT < 30 Ncm was applied,70,71 or even with a lack of IT (0 Ncm).72 However, these re-sults do not reflect the trend observed in human trials, where a correlation between the lack of rotational pri-mary stability and decreased overall survival rate was reported.73,74

As aforementioned, high IT has been linked to high-er stresses in the surrounding bone, which induces mi-crofractures, bone necrosis, and remodeling.17 Similar to what was previously reported with low IT, this idea has been challenged in a preclinical study, where im-plants placed under 110 Ncm survived for 6 weeks, reaching the peak of lesser stability after 7 days.75 In addition to this, a prospective investigation failed to demonstrate a statistical significance between mar-ginal bone loss in nonsubmerged implants using low (30 to 50 Ncm) and high torque (> 70 Ncm) 1 year af-ter placement.39 However, contrary to these findings, the results of a RCT displayed that implants placed with high IT (≥ 50 Ncm) in healed alveolar ridges had more peri-implant bone remodeling and buccal soft tissue recession than implants inserted with a regular IT (< 50 Ncm).76 In summary, the overall clinical out-comes elucidate the uncertain impact that IT may have


Monje et al

upon osseointegration and on the fate of the peri-implant bone. Generally, there is a linear correlation between the mechanical (primary) and the secondary stability; nevertheless, as demonstrated in the present systematic review, high IT is frequently associated with increased peri-implant bone loss.

On the Relationship of IT and Peri-implant Bone Stability from the Standpoint of Basic Biomechanics. To what degree is IT an accurate measure of implant mechanical (primary) stability? On the one hand, ev-eryday experience with tightening bolts and screws makes it intuitively attractive to think that “tighten-ing a screw” with torque is synonymous with achiev-ing “stability.” However, as discussed below, everyday experience with threaded fasteners is not always fully transferable to the case of dental implants in bone. A more complete discussion of the relationship between torque and stability involves familiarity with basic me-chanics underlying torque, interfacial pressure, misfit, mechanical properties of bone, and the very concept of stability itself. These topics are now discussed in more detail.

Starting with IT, an equation of Norton77 gives some initial insight (equation 1):

T = µπHD2p 2

Here T is insertion torque (IT), µ is the coefficient of friction between implant and bone, π is the con-stant pi, H is the length of the implant in contact with bone (which varies as an implant is inserted deeper into bone), D is implant diameter, and p is the pres-sure at the bone-implant interface. This equation 1 is based on assuming that: (1) the implant is a cyl-inder of uniform circular cross section with diameter D, and (2) the resistance to the applied torque T only arises from friction at the bone-implant interface, where the pressure, p, arises from a normal force caused by fitting the cylinder into its hole. Since this equation predicts a linear relationship between IT and interfacial pressure p, it confirms a clinician’s in-tuition that “more IT equals more pressure on bone.” However, this intuition is not fully correct if cutting occurs during placement of the implant; for exam-ple, in the case of an implant that is self-tapping, its placement also involves torque from cutting threads in the bone. The Norton equation also neglects other complexities arising from the size/shape of implant vs osteotomy, eg, using a tapered implant. Therefore, overall, the Norton equation provides limited insight into the origin of IT and its relationship to interfacial pressure. Moreover, it gives essentially no insight into any relationship that is sometimes presumed to ex-ist between IT and mechanical (primary) stability of dental implants.

Additional perspective on IT comes from examining the concept of “misfit” of an implant in its osteotomy. Skalak and Zhao78 defined misfit as the difference be-tween the radius of the implant and the radius of the osteotomy (r2 – r1) and derived an equation showing that the interfacial pressure (p) that develops between implant and bone depends upon misfit as well as the mechanical (elastic) properties of the bone and im-plant. Their equation for the interfacial pressure p is (equation 2):

p = (r2 – r1) [(1 + ν1)r1 +

(1 + ν2)(1 – 2ν2)r2]

–1

E1 E2

Here subscripts 1 and 2 refer to the bone in which the implant is placed and the material of the implant, respectively; likewise, the subscripted E and ν refer to Young’s elastic modulus and Poisson’s ratio of the bone and implant, respectively.

The Skalak-Zhao analysis is instructive because it reveals that the bone-implant interfacial pressure, p, is linearly proportional to both the misfit (r2 – r1) and the bone’s modulus (E1). This means that as either the mis-fit or the bone’s modulus increases, so does the inter-facial pressure. So as an example, for the same implant placed with the same misfit in “hard” (higher modu-lus) vs “soft” (lower modulus) bone, the pressure will be larger in the hard bone. Moreover, added insight comes from substituting Skalak-Zhao’s equation for p into the Norton equation, which yields the following equation for insertion torque (equation 3):

T = (r2 – r1) [(1 + ν1)r1 +

(1 + ν2)(1 – 2ν2)r2]

–1

(µπHD2

) E1 E2 2

This equation 3 predicts that for the same misfit (r2 – r1) and same implant material, the IT will increase with increasing elastic modulus of the bone. Notably, this prediction about IT is consistent with clinicians’ ex-perience as well as data from controlled experiments with implants in Sawbones.79

So how does this knowledge about IT relate to im-plant “stability”? This begs the question: What is the most direct and relevant measure of implant stability? As noted, several candidate measures have been sug-gested, but at present none of the available methods/devices provides a clinical measurement of how much an implant moves when the implant is loaded in the various ways that it may be loaded in vivo, eg, later-ally, axially, rotationally. The motivation for seeking a metric on implant movement under load is this: There is already significant support around the hypothesis that implant micromotion and related interfacial strain fields are key determinants of interfacial mechanobi-ology and related bone healing.6–11 So ultimately, this


Group 1

leaves the question of how IT—and related factors such as misfit and mechanical properties—do or do not relate to “stability” as best we can currently mea-sure it.

Currently, the most commonly used metrics of sta-bility are the RFA (Osstell) and percussion (Periotest) methods. For the sake of argument, we can focus on the Osstell method, which is based on excitation of small-magnitude implant vibrations (displacements) of the implant in bone.80,81 On this basis, it has been suggested that the ISQ value is sensitive to the local interfacial elastic properties (eg, the elastic modulus) of the bone surrounding the implant.82 So if this is true, and if, as already shown by the Skalak-Zhao and Norton analyses in equation 3, the IT depends on the elastic properties of the interfacial bone, then it fol-lows that there should be some relationship between IT and ISQ.

In exploring this, Bayarchimeg et al79 conducted experiments in which the same implant was placed with the same degree of misfit (r2 – r1) in samples of Sawbones having different values of the Young’s elas-tic modulus. They observed that IT and ISQ both in-creased with modulus, ie, IT correlated with ISQ. This result is also consistent with equation 3 on IT as well as Osstell’s background about the ISQ: both metrics should increase with elastic modulus of the bone.

However, what do IT and ISQ data say when it comes to the common clinical scenario in which a clinician at-tempts to increase the IT of an implant in a given sam-ple of bone (typically cancellous bone) by undersizing the osteotomy (ie, increasing the misfit)? In this clinical approach, does ISQ correlate with IT?

On the one hand, equation 3 predicts that IT will in-crease with increasing misfit. And in fact, this predic-tion is confirmed by experiments in both Sawbones (Bayarchimeg et al79) and porcine iliac bone samples in vitro.83 However, both of these studies showed that when implants were tested in the same modulus mate-rial but with increasing misfit, IT increased but ISQ did not83—ie, there was no correlation between IT and ISQ.

This last finding is significant because it is at odds with the common clinical practice of undersizing the osteotomy for an implant, ie, increasing misfit in porous cancellous bone—a practice that has evidently been premised on the intuition that “higher IT means higher stability.” The mechanics and data reveal, however, that this intuition is not always correct. On the one hand, equation 3 does reveal that IT should increase with in-creasing misfit. But on the other hand, the Osstell liter-ature indicates that ISQ (a popular metric for stability) should only increase if there is an increase in the elastic modulus of the interfacial bone; the Osstell literature is silent about whether misfit increases the modulus and therefore the ISQ. The simplest explanation of the

results of Bayarchimeg et al79 and Sakoh et al83—ie, no correlation between IT and ISQ when misfit increases in the same type of Sawbones or porcine bone—is that increasing misfit does not increase the peri-implant bone’s elastic modulus, which is a main determinant of the ISQ value. So while IT can increase from increasing misfit, that doesn’t mean the ISQ will increase.

On the Relationship of IT and Peri-implant Bone Stability at Molecular and Cellular Levels. From analyses of the mechanics underlying measurements of mechanical stability with methods such as RFA and Periotest, it is appreciated that these methods have some connection to implant micromotion,81 which in turn begs the question noted earlier of whether there is a threshold of micromotion below which osseoin-tegration can be guaranteed. Research has revealed, however, that micromotion needs to be understood at a deeper level; for example, it is not so much the micro-motion per se that is the key metric behind interfacial events but rather the local (microscopic) states of inter-facial strain that are engendered by the micromotion. Indeed, a number of recent papers have examined the role of interfacial strain as the decisive biomechanical variable in interfacial events.6–11 So at this time it is not possible to offer a reliable borderline between “safe” vs “dangerous” micromotion.

On the Accuracy of RFA to Monitor Peri-implant Tissue Stability. RFA was developed to provide a quantitative value for implant stability. Assuming that greater implant osseointegration should lead to higher stability, RFA was suggested as a tool useful to moni-tor peri-implantitis. Accordingly, it was shown that a linear relationship existed between peri-implant verti-cal bone defect depths.84–86 Furthermore, Sennerby et al showed in an experimental study in dogs that ISQ exhibited a linear correlation to radiographic peri-implant bone loss. It was further demonstrated that the ISQ values are dependent on the implant surface, showing a greater reduction with sandblasted acid-etched implants compared with turned implants.87 A recent canine study88 yielded findings that are in agree-ment with previous results. The baseline ISQ increased during the healing phase, and thereafter, the ISQ val-ues significantly decreased to 69.5 ± 1.30, showing a mean drop of 5.8%. The correlation between ISQ-MBL reached strong statistical significance (r = –0.58; P < .001). Nevertheless, it is important to note that the ligature-induced peri-implantitis applied in such study, courses with advance lesions. Therefore, as the ISQ val-ue remained high, the usefulness of RFA in monitoring progressive bone loss remains debatable. Due to the gap of knowledge in clinical studies, the present sys-tematic review could not address this question. Hence, this represents a promising research field to validate the use of RFA to monitor peri-implant bone loss.


Monje et al

CONCLUSIONS

Data suggest that primary stability leads to more effi-cient achievement of secondary stability. However, data are inconclusive concerning the effect of the degree of primary stability on implant survival and marginal bone loss. Furthermore, while insertion torque seems to influ-ence positively on implant survival, high thresholds of insertion torque have demonstrated to have a detrimen-tal effect on peri-implant marginal bone stability. The ac-curacy of the available methods and devices to assess implant stability remains to be answered.

DISCLAIMER

The authors have no direct financial interests with the products and instruments listed in the paper.

ACKNOWLEDGMENTS

The authors would like to thank the organizers and supporters of the conference leading to this paper, as well as the members of the working group on stability.

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s23a Volume 34, Supplement, 2019

SUPPLEMENTARY INFORMATION

Fig S1 Galbraith graph on marginal bone loss.

1.301.130.970.800.630.470.30

0 1 2 3 4

xi = 1

√νi + τ2

2

0

–2

z i =

yi

√νi +

τ2

0.20 0.60 1.00 1.40

0.000

0.037

0.074

0.111

0.148

Sta

ndar

d er

ror

Observed outcome

Fig S2 Funnel plot showing the dispersion of studies on mar-ginal bone loss.

Table S1 Newcastle Ottawa Scale for Nonrandomized Clinical Trials

Study (year) Selection Comparability Outcome/ Exposure

Alghamdi et al27 (2011) ★★ ★★ ★★★

Atieh et al28 (2014) ★★★★ ★★ ★

Balleri et al29 (2002) ★★ ★★ ★

Becker et al32 (2013) ★★★ ★ ★★

Olsson et al46 (2003) ★★★★ ★★ ★

Östman et al47 (2005) ★★ ★ ★

Östman et al49 (2013) ★★ ★ ★

Östman et al48 (2008) ★★★★ ★ ★★★

Pieri et al50 (2012) ★★ ★★ ★★

Rao and Benzi52 (2007) ★★ ★ ★

Sennerby et al54 (2012) ★★★★ ★★ ★★

Tatli et al57 (2014) ★★ ★ ★

Zix et al60 (2005) ★★★ ★★ ★★

Zwaan et al61 (2016) ★★★ ★★ ★

Calandriello et al33 (2003) ★★★★ ★★ ★★

Degidi et al34 (2012) ★★ ★★ ★

Grandi et al35 (2013) ★★★ ★★ ★★

Khayat et al39 (2013) ★★ ★★ ★

Maiorana et al42 (2015) ★★★★ ★★ ★★

Norton45 (2011) ★★★★ ★ ★★★

Rabel et al51 (2007) ★★ ★★ ★

Zwaan et al61 (2016) ★★★ ★★ ★★

Jeong et al37 (2015) ★★ ★ ★★★

Monje et al

The International Journal of Oral & Maxillofacial Implants S23b

Table S2 Bias Risk Assessment for the Included RCTs Using t Cochrane Risk of Bias Tool for Randomized Controlled Trials

Study (year)

Random sequence generation

Blinding of participants

and personnel

Blinding of outcome

assessment

Incomplete outcome

data addresses

Selective reporting

Other bias

Overall risk of bias

Bechara et al31 (2017) Low Low High Low Low Low Moderate

Karabuda et al38 (2011) Low Low Low Low Low Low Low

Kim et al40 (2015) Low High Low Low Low Low Moderate

Malchiodi et al43 (2016) Low High Low Low Low Low Moderate

Shayesteh et al55 (2013) Low High Unclear Low Low Unclear High

Turkyilmaz et al58 (2008) High High Unclear Low Low Unclear Low

Hof et al36 (2014) High Low Low Low Low Low Moderate

Al-Hashedi et al26 (2016) Low High Unclear Low Low Low High

GROUP 2

Inflammation and Osseointegration

Group Chair

Joseph Fiorellini, DMD, DMSc

Group Participants

Jeffrey Ackerman, DDS

Luigi Canullo, DDS, PhD

Satheesh Elangovan, BDS, DSc, DMSc

Stuart Froum, DDS

Michael Hotze, PhD

Richard Kao, DDS, PhD

E. Dwayne Karateew, DDS

David Kim, DDS, DMSc

Robert Lemke, DDS, MD

Jeffrey Lloyd, DDS

Kevin WanXin Luan, BDS, MS

Jay Malmquist, DMD

Myron Nevins, DDS

Georgios Romanos, DDS, DMD, PhD

Hector Sarmiento, DMD, MSc

Flavia Teles, DDS, MS, DMSc

Robert Vogel, DDS

Observers

Riddhi Gangolli, Straumann

Ben Grieve, Anker Dental Implant Company

Tom Olsen, Nobel Biocare



Peri-implant Mucosal Tissues and Inflammation: Clinical Implications

Joseph P. Fiorellini, DMD, DMSc1/Kevin WanXin Luan, BDS, MSc2/Yu-Cheng Chang, DDS, MSc3/ David Minjoon Kim, DDS, DMSc4/Hector L. Sarmiento, DMD, MSc5

Purpose: The purpose of this review was to explore the available literature and compile studies that

discuss the relevance of the biofilm, onset and progression of disease, critical peri-implant pocket depth,

frequency of supportive implant therapy, excess cement, and keratinized peri-implant tissues as related

to peri-implant disease. Materials and Methods: PubMed, Cochrane Oral Health Group Specialized Trial

Register, and hand searches of related journals were performed in relationship to the focused question.

Reports describing techniques, preclinical studies, and case reports were excluded. Results: Due to the

absence of controlled studies, a meta-analysis could not be performed. Summaries of relevant publications

were completed for each topic area. Clinical recommendations were developed to provide guidance to the

practitioner. Conclusion: The importance of proper diagnosis, planning, and clinical treatment cannot be

overstated. Patient factors including systemic disease, periodontal status, and oral hygiene significantly

impact peri-implant health. Clinician factors such as implant position, excess cement, and restorative design

can contribute to development of peri-implant disease. Surveillance of implant status is essential and can

be assisted by the assessment of risk factors, establishment of a proper recall program, and monitoring

changes in bone and peri-implant pocket depths. Int J Oral MaxIllOfac IMplants 2019;34(suppl):s25–s33. doi: 10.11607/jomi.19suppl.g2

Keywords: biofilm, cement, dental implants, inflammation, peri-implant disease, pocket depth, supportive implant therapy

Over the past almost five decades, dental implants have been a predictable therapy to replace miss-

ing teeth. The field has also evolved to include more

aggressive loading protocols, osseous augmentation of deficient bone areas, and modification of the im-plant designs. These, along with other factors, have led to an expansion in the number of implants placed and their more widespread use by both general practitio-ners and dental specialists. The importance of proper diagnosis, planning, and clinical treatment cannot be overstated. However, survival/success have been found to be affected by a number of risk factors re-sulting in peri-implant diseases. Peri-implant diseases have been characterized as a condition of the tissues around osseointegrated implants with signs of inflam-mation (bleeding and/or suppuration on probing) with or without loss of supporting bone.

Early on, Meffert (1992) described the ailing and failing implant.1 The ailing implant had bone loss with pocketing but was static at maintenance visits, whereas the failing implant also demonstrated bone loss with pocketing but presented additionally with bleeding on probing, purulence, and continued bone loss despite therapy. Misch (1998) also utilized clinical parameters to assess implant health.2 A continuum of health to disease was described with disease status re-lated to a progressive worsening of clinical parameters

1Professor, Department of Periodontics, University of Pennsylvania, School of Dental Medicine, Philadelphia, Pennsylvania, USA.

2Clinical Assistant Professor, Department of Periodontics, University of Illinois at Chicago, College of Dentistry, Chicago, Illinois, USA.

3Clinical Instructor, Department of Periodontics, University of Pennsylvania, School of Dental Medicine, Philadelphia, Pennsylvania, USA.

4Associate Professor, Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts, USA.

5Clinical Assistant Professor, Department of Periodontics, University of Pennsylvania, School of Dental Medicine, Philadelphia, Pennsylvania; Private Practice, New York, New York, USA.

Correspondence to: Dr Joseph P. Fiorellini, Department of Periodontics, University of Pennsylvania, School of Dental Medicine, 240 South 40th Street, Philadelphia, PA 19104-6030. Email: [email protected]

mailto:[email protected]


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such as probing depth, bone loss, and pain. Froum and Rosen (2012) established thresholds for clinical param-eters as a basis for classification of peri-implantitis.3 Combinations of bleeding on probing and/or sup-puration, probing depth, and radiographic bone loss were utilized to classify peri-implantitis into early, moderate, and advanced categories. Recently, a clas-sification system based on etiology was proposed.4 Its results indicated that the majority of bone loss was related to biofilm, followed by iatrogenic factors, ex-ogenous irritants, absence of keratinized tissue, and extrinsic pathology.

Although the literature denotes many risk factors associated with peri-implant diseases, the purpose of this review was to explore the available literature and compile studies that discuss the relevance of the biofilm, onset and progression of disease, critical peri-implant pocket depth, frequency of supportive implant therapy, excess cement, and keratinized peri-implant tissues as related to peri-implant disease.


RationaleThis systematic review evaluated the relationship of various risk factors as they pertain to dental implant disease.

Focused QuestionIn patients with dental implants, what is the relation-ship of biofilm, onset and progression of disease, criti-cal peri-implant pocket depth, frequency of supportive implant therapy, excess cement, and keratinized peri-implant tissues with peri-implant disease?

Search ProtocolPubMed and Cochrane Oral Health Group Specialized Trial Register were searched. Hand searches were per-formed on Clinical Oral Implants Research, The Interna-tional Journal of Oral and Maxillofacial Implants, The International Journal of Periodontics and Restorative Dentistry, Journal of Clinical Periodontology, Journal of Periodontology, and Journal of Periodontal Research.

Selection CriteriaPublications reporting mucositis of dental implants re-lated to biofilm, keratinized peri-implant tissues, peri-implant pocket depth, frequency of recall, and residual cement were included in the analysis. Reports describ-ing techniques were excluded.

Data Collection and AnalysisDue to the absence of controlled studies, a meta-analysis was not performed.

Biofilm and Inflammatory Response to Peri-implant DiseaseThe biofilm related to peri-implant diseases has not been extensively documented. The literature for peri-implant diseases follow those of gingivitis and peri-odontitis with regard to microflora and inflammatory response. As Loe et al (1965) induced an experimen-tal gingivitis,5 several studies have described the re-sponse around dental implants.6–9 They indicate that the response to the cessation of oral hygiene around implants and its onset are similar to that around the natural teeth. The classic signs of inflammation—Gin-gival Index, bleeding on probing, and Plaque Index—were increased. Salvi et al (2012) also induced an experimental peri-implant mucositis and found simi-lar results.6 However, the Gingival Index was higher around dental implants when compared to teeth. The authors postulated that the inflammatory response for dental implants is more severe than for the natural dentition. In addition, they found that with the rein-troduction of oral hygiene measures, the soft tissues recovered to baseline values.6 Zitzmann et al (2001) evaluated the cellular response with an experimen-tally induced peri-implant mucositis with tissue biop-sies.7 The response of T- and B-cells was similar to that around teeth. At 21 days, there was an increase in the volume of these lymphocytes. Lastly, host biomarkers including IL1-β, TNF-α, and TGF-β2 have been evalu-ated in the induced peri-implant mucositis model.10 Only IL-1β increased over the 3-week period in the gingival crevicular fluid. Following reinstitution of oral hygiene, IL1-β levels returned to baseline. In natural longstanding peri-implant mucositis lesions, sites that bled on probing and had some degree of redness and inflammation demonstrated on biopsy infiltrate in the connective tissue.11

The biofilm conversion from peri-implant mucosi-tis to peri-implantitis has been only documented with cross-sectional studies describing the characteristics of peri-implantitis. The microbiome of healthy and/or diseased implants have been compared.12,13 In gener-al, species of the red complex such as Porphyromonas gingivalis and Tannerella forsythia have been found in diseased sites. Sanz-Martin et al (2017) studied healthy and diseased implants, illustrating that both states had a core microbiome, while health had taxa consistent with periodontal health and peri-implantitis had taxa consistent with periodontitis.14 In addition, the find-ings regarding peri-implantitis biofilm have shown species including Synergistetes and Tannerella. In a sys-tematic review by Lafaurie et al (2017), peri-implantitis was an infection with a diversity of microorganisms including periodontal pathogens, anaerobic Gram-negative rods, and rarely, entric rods and Staphylo-coccus aureus.15 Overall, the majority of information


Fiorellini et al

available indicates that a shift occurs from health to disease with regard to the biofilm. The biofilm associat-ed with the diseased dental implant seems to be more pathogenic and somewhat like those in periodontitis.

Clinical Recommendations• The clinician should be aware that a shift in the

biofilm and inflammatory response occurs with the conversion of a healthy to diseased implant state. For example, with the deepening of a peri-implant pocket, the practitioner should consider additional measures to monitor and/or treat the failing implant.

• The biofilm associated with peri-implant disease may be similar to that in periodontitis. As a result, the susceptible periodontitis patient with dental implants should be carefully monitored.

• Given the inflammatory basis of peri-implant diseases, the surgical and restorative clinician should carefully consider the contour, function, and overall cleansability of the prosthesis throughout the planning process.

Time to Onset and Progression of Peri-implant DiseasesThe development of peri-implant diseases seems dependent on many variables, such as oral hygiene, quality of tissue, and restoration type. However, unlike periodontitis, factors such as position of the implant within the edentulous ridge, implant surface texture, etc, impact the development of both mucositis and/or peri-implantitis. In addition, the pattern and rate of onset do not follow that of periodontal disease but may be influenced by the presence or history of periodontitis.

Studies involving experimentally induced peri-implantitis demonstrate clinical changes as in an induced gingivitis.6 The cessation of oral hygiene produced increases in plaque, Gingival Index, and bleeding. When oral hygiene was reinstituted, clinical parameters recovered.8 The analysis of biologic re-sponses to the induction of peri-implant mucositis in-dicates an upregulation of peri-implant crevicular fluid factors and the cellular response involving an increase in the proportions of T and B cells.7 Overall, these stud-ies indicate that the clinical biologic response as well as timing of progression in the experimental induction of peri-implant mucositis collates with induced gingivitis.

In a retrospective review of dental radiographs, Fransson et al (2010) evaluated the onset and progres-sion of bone loss.16 A total of 182 patients with 419 implants were assessed for bone loss and had a mean follow-up period of 11.1 years. Bone loss after the first year averaged 1.68 mm. Interestingly, 32% of the im-plants had bone loss of ≥ 2 mm, and 10% of those had bone loss ≥ 3 mm. Analysis indicated that progression

was nonlinear and the rate decreased with time. Derks et al (2016) also found that the pattern of bone loss was nonlinear.17 A retrospective review of 105 implant dental radiographs indicated that the onset generally occurred within the first 3 years of function. However, compared to Fransson et al (2010),16 the amount of bone loss was greater with an average of 3.5 mm. Bone loss of more than 3 mm occurred in 51% of the sample. The authors postulated that the bone loss could be related to a prior history of periodontitis. In a shorter-duration study, Schwarz et al (2017) found that of 512 implants, a total of 262 were diagnosed with peri-im-plant disease.18 The majority of disease that occurred with implants was found between 12 and 48 months. After 48 months, the rate of disease was similar to that between 1 and 12 months. These results again indicate that peri-implant disease occurs early in the life of a functionally loaded implant. Recently, Sarmiento et al (2018) found that the timing of grafting influenced the rate of peri-implant disease.19 In a staged grafting pro-cedure where the implant is placed following a heal-ing period, the rate of peri-implant mucositis was 8.1% and the rate of peri-implantitis was 4.4%. However, when grafting occurred at the time of implant place-ment, the rate of peri-implant mucositis was 7.5% and the rate of peri-implantitis was 9.7%. In addition, the onset of mucositis preceded the statistical detection of radiographic bone loss.

Clinical Recommendations• Although limited, studies indicate that the onset and

the majority of peri-implant disease progressions occur in the life of a functionally loaded implant generally prior to 36 months.

• Since the onset and progression of peri-implant diseases seem not to follow patterns of periodontitis, the clinician should have an enhanced awareness of risk and should structure an individualized recall program for the patient.

Critical Peri-implant Pocket DepthThe establishment of the biologic width with implant healing has been well documented. Although the di-mensions seem to vary when compared to the natural dentition, the formation of an epithelial and connec-tive tissue attachment occurs. The healing results in the formation of a peri-implant sulcus. Several fac-tors, such as surface characteristics, implant height above the bone level, platform switching, microgap, abutment size, etc, influence the final measurements. With the onset of peri-implant disease, these struc-tures begin to change much in the same manner as with periodontitis. Gingival inflammation can increase the peri-implant sulcus and with loss of supporting bone, apical migration of the connective tissue and


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epithelium occurs. The disease around an implant with bone loss then forms a deepened peri-implant pocket.

Healthy peri-implant mucosa can be altered by fac-tors that progress to conditions of peri-implant muco-sitis and peri-implantitis. Although clinical assessment can differentiate between a state of health and dis-ease, a critical implant pocket depth can assist in defin-ing this transitional state. Derks et al (2016) described healthy peri-implant tissue by the absence of bleed-ing on probing, mucositis as presence of bleeding on probing ± suppuration without radiographic bone loss, and peri-implantitis as bleeding on probing ± suppu-ration and radiographic bone loss > 0.5 mm (45% of cases presented).17 The onset of disease occurred early, with the majority of peri-implantitis cases/implants oc-curring within 3 years of function. Schwarz et al (2018) measured peri-implant sulcus/pocket depth values of 1 to 3 mm and 4 to 6 mm for healthy and mucositis im-plants, respectively.20 Peri-implantitis implants only re-corded pocket values in the 4- to 6-mm range. Schwarz et al (2017) noted that mucositis and peri-implantitis could present within 12 to 48 months after initial im-plant placement.18

Recently, Monje et al (2018) evaluated 1,572 sites around 262 implants in 141 patients.21 The clinical parameters included the evaluation of pocket depth. In this cross-sectional matched case-control study, healthy implants had a mean pocket depth of 2.63 mm, mucositis-classified implants had a mean pocket depth of 3.26 mm, and peri-implantitis–diagnosed implants had a mean pocket depth of 4.58 mm. The authors concluded that pocket depth might accurately discern between diagnoses among peri-implant conditions. Similarly, Ramanauskaite et al (2018) evaluated 269 dental implants with several different clinical param-eters including peri-implant pocket depth.22 A total of 77 dental implants diagnosed as healthy had a mean pocket depth of 2.95 mm. Peri-implant mucositis im-plants (n = 77) had a mean pocket depth of 3.10 mm, whereas peri-implantitis implants (n = 115) had a mean pocket depth of 4.91 mm. These differences in pocket depth between the three groups were signifi-cantly different.

Clinical Recommendations• The documentation of dental implant health status

is an important component of clinical care. During the treatment process, a baseline radiograph (at final prosthesis insertion) and clinical parameters (pocket depth, bleeding on probing, etc) should be recorded. The frequency of periodic documentation should be based on individual risk factors in order to assess changes in implant health over the life of the implant.

• The apparently healthy implant may still harbor an environment (biofilm, inflammation, etc) that could predispose the implant to convert to disease. Studies indicate that the clinician should be aware of the development of future markers for peri-implant disease and conditions including perfuse bleeding on probing, suppuration, bone loss, and critical implant pocket depth of ≥ 5 mm. This awareness includes but is not limited to continuing routine care, radiographic assessment, oral hygiene instruction, and shortened recall interval.

Frequency of Supportive Implant TherapyAccording to contemporary studies, there is an esti-mated 43% prevalence of peri-implant mucositis and a 22% prevalence of peri-implantitis.23–25 A majority of this peri-implant disease may share a pathogenesis with periodontitis.26 Therefore, peri-implant mainte-nance seems to be critical to maintain the stability of the tissue around dental implants.27,28

Despite the understanding of the importance of peri-implant maintenance, there have been no well-controlled investigations that refine the peri-implant recall interval; this is due to various definitions of diag-nosis, unclear terminology, varying follow-up periods, and ethical considerations. Thus, most of the evidence of supportive implant therapy interval has been ob-tained from retrospective studies or reviews.

The prevalence rates of peri-implant diseases were evaluated in 89 patients.29 The patients were assigned to 3-month intervals during the first year after im-plant placement and later on 6-month intervals. Pa-tients who did not participate in regular supportive implant therapy had an 11-fold higher chance of peri-implantitis than patients showing good compliance. On the contrary, patients who did not have regular supportive implant therapy were reported to have up to 48% of the prevalence of peri-implant mucositis during an observation period of 9 to 14 years.30 These studies have confirmed the essential role of supportive implant therapy to maintain tissue health and that the lack of supportive implant therapy will lead to a higher prevalence of the peri-implant disease.

A retrospective study done by Frisch et al (2015) evaluated the compliance and the frequency of the supportive post-implant therapy program and indi-cated a positive correlation between lower compliance and increased probing depth and higher plaque rate.31 The compliance rates were categorized into five differ-ent groups, from a 3-month interval to no compliance at all. The higher rates of patient compliance (86% to 94%) were observed during the first 3 years. A signifi-cant correlation was found between lower compliance and increased peri-implant probing depth. In addition,


Fiorellini et al

higher plaque rates were found in individuals with lower compliance rates.

Dental implant patients may have a higher risk of peri-implant disease due to diabetes, poor oral hy-giene, and smoking.29,32–34 A history of periodonti-tis seems to be the most documented risk for future peri-implant disease.28,35,36 Long-term studies done by Roccuzzo and coworkers evaluated the tissue around dental implants with supportive implant therapy for 10 years.37–39 The study found that in periodontally healthy patients there were no statistical differences in clinical parameters if subjects adhered to their sup-portive implant therapy or not. In contrast, the patients who had moderate to severe periodontal disease had higher plaque scores, bleeding scores during the sup-portive implant therapy, and eventually implant loss. In a systematic review, Monje et al (2016) analyzed 13 studies to evaluate the impact of supportive main-tenance on the implant.40 This review successfully provides positive evidence of the patient with mainte-nance and concluded a minimum recall post-implant maintenance therapy of 5 to 6 months. However, the studies did indicate that the possibility of biologic complications might still occur and other risk factors should be appropriately considered.

In 2013, Aguirre-Zorzano et al stated that the preva-lence of peri-implant inflammatory disease in peri-odontal patients who regularly undergo supportive implant therapy with a mean recall of 4 months is clini-cally significantly lower and the peri-implant disease could be even prevented.41 A similar result was found in a retrospective study by Costa et al (2012), which suggested that the simple fact of enrolling subjects for supportive implant therapy may reduce the risk of peri-implantitis from 43.9% to 18% at the patient level with maintenance at least once a year.42 These findings provide clinical evidence that the supportive implant therapy interval should be adjusted case by case, par-ticularly in patients with a history of periodontitis.

Clinical Recommendations• In general, a reasonable interval of supportive

implant therapy is 5 to 6 months for a patient with low risk of peri-implant disease, but should this be evaluated case by case.

• When considering a patient with a history of risk, such as periodontal disease, it may be necessary to shorten the supportive implant therapy interval.

• During a supportive implant therapy appointment, reinforcement of oral hygiene, modification of factors such as smoking, and removal and cleaning of the prosthesis should be considered.

Role of Excess Cement in Peri-implant DiseasesCemented restorations are commonly used to restore dental implants. It has been shown that excess cement left in the sulcus around implant-supported restora-tions can cause inflammation, ultimately leading to peri-implant disease. Cemented restorations offer sev-eral advantages, such as ease of prosthetic fabrication, reduced costs, increased framework passivity, and improved esthetics due to the absence of the buccal screw access hole. The main disadvantage of cement-retained restorations is the fact that cements are a flowable material that can spread unintentionally to the adjacent gingival tissues, making it difficult for the clinician to properly remove excess subgingival ma-terial. Therefore, caution on implant selection, place-ment, and prosthetic design should play a key role in the initial treatment plan. Although we aim for screw restoration as a primary election to restore a dental implant, it is not always feasible if angulation of the implant body is necessary.

A study published by Wilson (2009) demonstrated how the presence of peri-implantitis caused by dental cements was observed in 81% of cases.43 Korsch and Walther (2015) compared various types of cements and associated peri-implant disease. The authors found that the frequency of excess cement depended on cement type.44 For implants that used methacrylate cement, the frequency of excess cement was 62%, whereas the frequency of excess cement for zinc oxide-eugenol ce-ment was 100%. Although several publications have documented different peri-implant complications, few have addressed the causative factor for the biologic breakdown. In a study published by Sarmiento et al (2016), the classification system based on etiologies included 5.5% of cases with peri-implantitis induced by an exogenous factor, eg, residual excess cement.4 Another retrospective study by Linkevicius et al (2013) concluded that patients with previous periodontal dis-ease may also be more predisposed to peri-implantitis due to excess cement.45 Other studies have shown that the presence of suppuration around dental implants was greater on crowns that were cemented versus screw-retained.46 Many authors have shown different techniques to reduce excess cement complications. Linkevicius et al (2013) in a clinical study showed that dental radiographs should not be considered as a reli-able method for cement excess evaluation.47 His results revealed that cement remnants were around 7.5% to 11.5% depending on the location of detection. Wad-hwani et al (2010) proved that radiographic density of implant restorative cements is poor and depending


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on the thickness of the cement, smaller pieces would remain unseen.48 They suggested placing margins su-pragingival for proper cement removal.

Challenges for the clinician on when to place a cement-retained restoration should not be based on material selection to achieve higher esthetics, but rath-er the criteria should be those unique to cementation technique and proper treatment planning. Therefore, avoiding cement-retained restorations in difficult-to-access areas should be recommended for clinicians with less experience.

Clinical Recommendations• All attempts to reduce excess submarginal cement

should be made through the use of screw-retained restorations or abutments designed to minimize the depth of the cement line in relation to the peri-implant tissue architecture. The cement line should be located with a minimal depth for ease of removal and to minimize submarginal excess cement.

• A final radiograph should be carried out routinely to assess unwanted cement beyond the gingival margin.

• Postoperative assessment is recommended within the first weeks post–crown insertion. This should allow immediate clinical detection of excess cement and avoid peri-implant tissue inflammation.

Role of Keratinized Tissue Surrounding Dental ImplantsPeri-implant health has been defined both clinically and histologically.49 Peri-implant tissues surround an osseointegrated dental implant and can be divided into hard and soft tissue components. The hard tissue component forms a contact relationship to the im-plant surface, which contributes to implant stability.50 The soft tissue component is formed during the heal-ing process following implant/prosthetic placement.51 The peri-implant tissues protect the bone that sup-ports the implant. With the absence of healthy peri-implant tissues, the long-term implant success and survival becomes compromised and less predictable.42 However, if there is an insufficient amount of keratin-ized tissue around implants, is there an increased risk of peri-implant mucositis? If so, what types of modali-ties are appropriate to address a lack of keratinized tis-sue of the peri-implant tissues?

Does the Absence of Keratinized Tissue Influence Peri-implant Mucositis? Keratinized tissue or mucosa, which extends from the margin of the peri-implant mucosa to the mucogingival junction, is composed of fibrous connective tissue with fibroblasts; type I, III, IV, V, and VI collagen; and an orthokeratinized squamous epithelium.52,53 Keratinized gingiva has been defined

as marginal and attached gingiva that excludes soft tissue of the interdental col region—interproximal gin-gival tissue between posterior teeth where epithelium is devoid of keratinization.54 One theory explaining the reduction of keratinized tissue is the post–tooth extrac-tion natural loss of crestal bone. The buccal thickness of keratinized tissue is greater at the base of implants than at teeth (2.0 mm vs 1.1 mm, respectively).53 Ac-cording to a new classification scheme for periodontal and peri-implant diseases and conditions, peri-implant mucositis is defined as an inflammatory lesion of the mucosa surrounding an endosseous implant with-out loss of supporting peri-implant bone.34,55,56 This is clinically determined by the presence of redness, swelling, bleeding on probing, and suppuration. The dimensions of peri-implant keratinized mucosa may be a risk indicator for peri-implant mucositis. The need for a minimum amount of keratinized tissue in order to maintain peri-implant tissue health has been a controversial issue.57–61 Some studies suggested that plaque accumulation that resulted in marginal inflammation was more frequent at implant sites with < 2 mm of keratinized tissue.62–66 However, several studies suggested that the lack of a minimum amount of keratinized tissue was not associated with mucosal inflammation.64,67–72

A systematic review assessed seven cross-sectional and four longitudinal studies to determine if kera-tinized mucosa affected implant health, suggesting that a lack of adequate keratinized tissue around en-dosseous dental implants is associated with plaque accumulation, tissue inflammation, recession, and at-tachment loss.58 This supported a meta-analysis that indicated a statistically significant difference between plaque scores and modified Gingival Index in favor of sites with a wider dimension for keratinized tissue.59 The width of keratinized tissue at implant sites is an-other area of controversy.73 The association of keratin-ized mucosa width at implant sites was studied in a small group of patients 5 to 10 years retrospectively. Statistical analysis failed to indicate an association be-tween keratinized tissue or the mobility of marginal mucosa around implant sites with plaque accumula-tion, bleeding on probing, or probing depth.69 An-other longitudinal study of 339 implants measuring the amount of keratinized mucosa present over a pe-riod of at least 3 years showed a higher Gingival In-dex (0.9 vs 0.8) and modified Plaque Index (1.5 vs 1.3) in patients with keratinized tissue with < 2 mm and > 2 mm, respectively.62 A 5-year study involving 307 implants in edentulous mandibles with fixed implant-retained reconstructions assessed sites with < 2 mm and > 2 mm of keratinized tissue. The investigators reported higher plaque scores (0.7 vs 0.4) and bleed-ing on probing (0.2 vs 0.1) at lingual sites as well as


Fiorellini et al

recession (0.7 vs 0.1) at buccal sites.72 Another study involving 15 patients with mandibular overdentures on four implants assessed the presence or absence of keratinized tissue on buccal aspects of implants, showing that 19 implants with at least 2 mm of ke-ratinized mucosa had lower plaque (0.3 vs 0.6) and gingival indices (0.1 vs 0.6) than 17 implants without keratinized mucosa.66 It has been suggested that the evidence is equivocal regarding the effect of keratin-ized tissue on the long-term health of the peri-implant tissue. The notable advantages are the ease of plaque removal and patient comfort.74–78

Does Grafting Help in the Management of Peri-implant Mucositis? Historically, scientific evi-dence reported that a lack of keratinized tissue was not critical to maintain peri-implant soft tissue health nor to result in more peri-implant diseases.69,79 However, based on current evidence, it has been suggested that an increased amount of keratinized tissue may better preserve both soft and hard tissue stability, resulting in a favorable long-term outcome for dental implants (as well as better oral hygiene maintenance over time).63,72,73 Also, an increased amount of keratinized tissue thickness may decrease the risk of recessions with immediate implants.80 Therefore, periodontal sur-gical procedures that augment soft tissue volume are recommended for esthetic and dimensional advantag-es following tooth extraction and implant therapy for both immediate and delayed placement.81–84

Bleeding on probing was discussed in two studies with respect to grafting and nongrafting treatments af-ter implant placement. According to a long-term study by Roccuzzo et al, there was an insignificant difference (23% and 27%) between groups with or without soft tissue grafting.65 However, in another study there was notable improvement from mean baseline values of 85% to 30% with autogenous soft tissue grafting com-pared with 40% to 95% to 25% to 95% without soft tissue grafting.83 In the same study, the mean Gingival Index shared a similar notable improvement compar-ing soft tissue grafting with no soft tissue grafting after follow-up periods of 6 to 12 months. Multiple studies indicated that there was a significant benefit to lower plaque values following surgical intervention when increasing keratinized tissue.65 Conversely, one study compared Plaque Index of treated and untreated groups over time and found no significant difference at baseline and at 12 months.85

With regard to probing depth, there were no signifi-cant changes over time between the different treat-ment groups of apically positioned flap versus apically positioned flap plus free gingival graft in a meta-anal-ysis.86 The mean probing depth values at baseline for soft tissue grafting, 1.97 to 3.09 mm, reduced to 2.08 to 3.18 mm after 6 to 12 months, while no soft tissue

grafting ranged from 1.76 to 3.25 mm at baseline and 1.60 to 3.62 mm after 6 to 12 months. Comparing these final probing depth values favored the apically posi-tioned flap plus autogenous tissue. In a clinical study of 30 patients with keratinized tissue of < 1 mm at im-plant sites, half of the patients underwent surgery to widen the band of keratinized mucosa. After 10 years, there was a significant difference in the gain of keratin-ized mucosa of 3.1 mm versus 0 mm in patients who underwent surgery and patients who did not, respec-tively. However, the Plaque Index, bleeding on prob-ing, and presence of peri-implantitis was not different between both groups.31 Lastly, a meta-analysis assess-ing three different treatment modalities (autogenous, collagen matrix, and apically positioned flap) used for implant site maintenance with > 2 mm, < 2 mm, or no keratinized tissue resulted in statistically significant differences favoring apically positioned flap plus au-togenous tissue65 in a period of 6 months.

Although there has been an increase of literature to support the use of soft tissue augmentation for keratinized tissue around peri-implant tissues, un-fortunately there is a lack of data regarding clinical long-term outcomes associated with peri-implant soft tissue augmentation procedures.

Clinical Recommendations• The removal of plaque around peri-implant tissues

should be performed as part of a routine periodontal maintenance program to prevent the progression of peri-implant diseases and conditions.

• Soft tissue augmentation surgery should be performed around dental implants to provide stable keratinized tissue.

CONCLUSIONS

The importance of proper diagnosis, planning, and clinical treatment cannot be overstated. Patient factors including systemic disease, periodontal status, and oral hygiene significantly impact peri-implant health. Clini-cian factors such as implant position, excess cement, and restorative design can contribute to development of peri-implant disease. Surveillance of implant status is essential and can be assisted by assessment of risk factors, establishment of a proper recall program, and monitoring changes in bone and peri-implant pocket depths.

DISCLAIMER

The authors have no direct financial interests with the products and instruments listed in the paper.


Group 2

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GROUP 3

Systemic Health, Medications, and Osseointegration

Group Chair

Tara Aghaloo, DDS, MD, PhD

Group Participants

Gustavo Avila-Ortiz, DDS, MS, PhD

Nadim Baba, DMD, MSD

Susan Bukata, MD

Ting-Lin Chang, DDS

Steven Eckert, DDS, MS

Robert Eskow, DMD

Brett Ferguson, DDS

Henry Ferguson, DMD

Jeffrey Ganeles, DMD

Wendy Croll Halpern, DMD

Andrea Henderson, DDS

Fouad Khoury, Prof, DMD, PhD

Alireza Moshaverinia, DDS, MS, PhD

Joerg Neugebauer, DDS, PhD

Joan Pi-Anfruns, DMD

Robert Taft, DDS

Faleh Tamimi, BDS, PhD

James Taylor, DMD, MA



The Effects of Systemic Diseases and Medications on Implant Osseointegration: A Systematic Review

Tara Aghaloo, DDS, MD, PhD1/Joan Pi-Anfruns, DMD2/Alireza Moshaverinia, DDS, PhD3/ Danielle Sim, BS4/Tristan Grogan, MS5/Danny Hadaya, DDS6

Since their development, dental implants have become one of the most common procedures to rehabilitate

patients with single missing teeth or fully edentulous jaws. As implants become more mainstream,

determining the factors that affect osseointegration is extremely important. Medical risk factors identified

to negatively affect osseointegration include diabetes and osteoporosis. However, other systemic conditions

and medications that interfere with wound healing have not been as widely investigated. The aim of this

systematic review was to evaluate the effect of systemic disorders including diabetes and osteoporosis on

implant osseointegration. The aim was also to evaluate the effect of other diseases, such as neurocognitive

diseases, cardiovascular disease, human immunodeficiency virus (HIV), hypothyroidism, rheumatoid arthritis,

and medications, such as selective serotonin reuptake inhibitors (SSRIs), proton pump inhibitors (PPIs), and

antihypertensives. Although the literature does not demonstrate that diabetes negatively affects implant

osseointegration, most studies focus on well-controlled diabetics and the use of prophylactic antibiotics.

In addition, studies have shown increased long-term bone and soft tissue complications. For osteoporosis,

recent studies and reviews also fail to demonstrate a lower osseointegration rate. However, caution must

be exercised in these patients due to the risk for osteonecrosis of the jaws (ONJ), especially in patients with

bone malignancies. There is also no direct evidence that patients with HIV, cardiovascular disease, neurologic

disorders, hypothyroidism, or rheumatoid arthritis have a decreased rate of implant osseointegration.

However, some preliminary evidence suggests that medications such as SSRIs or PPIs may have a negative

effect on implant osseointegration. These studies are fairly recent and must be validated with continuous

research. Moreover, disease control, concomitant medications, and other comorbidities complicate implant

osseointegration and must guide our treatment approaches and clinical guidelines. Int J Oral MaxIllOfac IMplants 2019;34(suppl):s35–s49. doi: 10.11607/jomi.19suppl.g3

Keywords: diabetes, medical risk factors, osseointegration, osteoporosis

According to the National Center for Health Statistics (NCHS), more than 36 million Americans are eden-

tulous, and 90% of them have dentures.1 An additional 120 million are missing at least one tooth, and it is es-timated that in the next 15 years, tooth loss will affect more than 200 million individuals. Tooth loss can be a result of decay, periodontal disease, wear, trauma, or cancer, and it can lead to additional dental problems, nutritional changes, and social and personal self-es-teem issues. Although complete and partial dentures and tooth-borne prostheses are still options to replace missing teeth, dentists and patients often demand a more advanced and integrated solution.

Osseointegration is a direct contact between liv-ing bone and a titanium implant.2 Although initially a complex interaction between living tissue and metal to replace missing teeth and facilitate comprehensive dental and craniofacial rehabilitation, implants are now one of the most common procedures performed in dentistry today. Multiple short- and long-term stud-ies demonstrate that dental implants are safe and

1Professor, Division of Diagnostic and Surgical Sciences, Section of Oral and Maxillofacial Surgery, UCLA School of Dentistry, Los Angeles, California, USA.

2Assistant Clinical Professor, Division of Diagnostic and Surgical Sciences, Sections of Oral and Maxillofacial Surgery and Restorative Dentistry, UCLA School of Dentistry, Los Angeles, California, USA.

3Assistant Professor, Division of Advanced Prosthodontics, Section of Prosthodontics, UCLA School of Dentistry, Los Angeles, California, USA.

4Research Assistant, Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.

5Principal Statistician, Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.

6PhD Candidate, Division of Diagnostic and Surgical Sciences, UCLA School of Dentistry, Los Angeles, California, USA.

Correspondence to: Dr Tara L. Aghaloo, UCLA School of Dentistry, 10833 Le Conte Ave CHS Rm 53-009, Los Angeles, CA 90095-1668. Fax: (310) 825-7232. Email: [email protected]

mailto:[email protected]


Group 3

effective.3–5 Osseointegration classically occurs after 3 to 6 months with machined or turned titanium implant surfaces; it may occur faster with enhanced or rough-ened surfaces.6–8 This improved osseointegration has sparked an increased use of dental implants, with early or immediate placement and loading, and implants in patients with medical risk factors.9,10 However, stud-ies have been unclear and conflicting in terms of os-seointegration or short-term survival between healthy and medically compromised patients.11–14 Many stud-ies have identified specific medical conditions such as diabetes mellitus and osteoporosis as negative fac-tors for both osseointegration and long-term implant survival.14–17 As such, these long-term studies do not examine whether initial osseointegration was affected by systemic diseases.

Diabetes mellitus (DM) is a chronic metabolic disor-der that can present in two forms. Patients who suffer from type 1 DM have an autoimmune disorder where their body cannot produce insulin; as such, these in-dividuals must take exogenous insulin to control their blood sugar. Type 2 DM is a multifactorial disease, where a combination of genetics and environment deem the pancreas unable to produce insulin and lead the body to become insulin resistant.18,19 In both con-ditions, chronic high levels of circulating glucose lead to more severe complications, which affect numerous organ systems, including the oral cavity. This condi-tion has classically been associated with an increased risk of dental implant failure, with numerous studies suggesting poor osseous healing in times of hyper-glycemia.20–22 Indeed, diabetics suffer from increased incidence of periodontitis and gingivitis, and are more subject to dental infections.23 Despite the lack of clini-cal evidence, these associations are made and may often limit patient treatment, especially since some previous studies have demonstrated increased short-term implant failure in diabetic patients or increased time required for osseointegration.14,24,25

Osteoporosis is a musculoskeletal disease of re-duced bone mass, low bone mineral density with a T-score ≤ –2.5 standard deviations of lumbar spine (L1-L4) and femoral neck, and susceptibility to bone fractures.26–28 Although the diagnosis of osteoporosis includes decreased cortical bone and thin trabecular bone with increased trabecular spacing in the axial and appendicular skeleton, correlation with decreased bone mineral density or thin trabecular and cortical bone in the maxilla and mandible has not been consis-tent.29–32 Moreover, the relationship between systemic bone density and metabolism is not well understood.33 Indeed, patients with systemic osteoporosis are often seeking dental implant therapy, and studies have sug-gested an increased failure in those patients, espe-cially in the maxilla.14,34–36 In fact, type IV bone with

decreased cortical and increased trabecular bone has been associated with higher implant failure.37,38 How-ever, more recently, as enhanced implant surfaces are utilized almost exclusively, osseointegration and long-term peri-implant bone maintenance is favorable in osteoporotic patients.39–42

As more of our implant patients have significant medical problems and take medications that may af-fect wound healing and bone metabolism, we must be able to evaluate the literature in a systematic way where results can be combined to maximize the in-formation currently available. Therefore, the aim of this systematic review was to evaluate the effect of systemic disorders, including diabetes and osteoporo-sis, on implant osseointegration. Also evaluated were the effect of neurocognitive diseases, cardiovascular disease, human immunodeficiency virus (HIV), hy-pothyroidism, and rheumatoid arthritis (RA), as well as medications such as selective serotonin reuptake inhibitors (SSRIs), proton pump inhibitors (PPIs), and antihypertensives.


Study Selection and Inclusion CriteriaThe focused question was developed following the PICO format (Population: patients with osteoporosis or diabetes, or patients taking systemic medications; Intervention: dental implant therapy; Comparison: healthy patients; Outcome: osseointegration) to evalu-ate the effect of systemic health conditions and medi-cations on implant osseointegration. Inclusion criteria were determined by the authors before the beginning of the study: human or clinical studies with at least 10 patients, publication in the English literature, a follow-up time of ≥ 3 months to assess implant osseointegra-tion, and defined specific medical problems with data on implant survival (surrogate marker of osseointegra-tion). Studies were excluded if they were animal or in vitro studies, case reports, or small case series; if they lacked specific information on implant survival or os-seointegration, specific numbers of patients with sys-temic illness or medication, or specific numbers of patients and/or implant survival; or were published in non–English-language journals (Table 1).

Search StrategyFor this systematic review, PRISMA (Preferred Report-ing Items for Systematic Reviews and Meta-Analyses) guidelines were utilized.43 A PubMed electronic search was performed to identify potential articles from 1975 to July 2018 utilizing the keywords of different combinations: “dental implants” and “osteoporosis,” “bisphosphonates,” “diabetes,” “parkinson’s disease or


Aghaloo et al

neurocognitive disease,” “rheumatoid arthritis,” “cardio-vascular disease or hypertension or coronary artery disease,” “hypothyroidism,” “human immunodeficiency virus or acquired deficiency syndrome,” “depression,” ”anti-hypertensive or diuretics or beta-blockers,” “sero-tonin reuptake inhibitors,” “proton pump inhibitors,” or “thyroid.” In addition to the online search, hand search was performed in specific journals, including The In-ternational Journal of Oral and Maxillofacial Implants; Clinical Oral Implants Research; Journal of Oral and Max-illofacial Surgery; International Journal of Oral and Max-illofacial Surgery; Clinical Implant Dentistry and Related Research; Implant Dentistry; Journal of Periodontology; Journal of Clinical Periodontology; and Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodon-tics. Titles were screened by two independent investi-gators (TLA and JPA), and any disagreements between the two authors were resolved by all four clinicians of this review.

Statistical AnalysisFor the meta-analysis, forest plots were constructed us-ing the proportion of implants healed for both the dia-betic cohort as well as the osteoporotic cohort (which was then stratified by medication usage). Heterogene-ity was assessed using the Cochran’s Q statistic and I2 index (the percentage of variation across studies that is due to heterogeneity rather than chance).44 Since significant heterogeneity was observed, the authors elected to use the random effects meta-analytical model as opposed to the fixed effects pooled esti-mate. Statistical analyses were performed using R V3.5.1 (www.r-project.org) utilizing the ‘meta’ package. P values < .05 were considered statistically significant.

RESULTS

The analysis of studies for patients with osteoporo-sis revealed a total of 408 studies, with 21 studies re-maining for analysis after further assessment (Fig 1). Four studies were included for osteoporotic patients not on antiresorptive medications (Table 2), and 17 studies were included for patients taking antiresorp-tives (Table 3). For osteoporotic patients, the pooled

estimate for implant survival rate from the meta-analysis was 98% (with a 95% confidence interval [CI] of 96%–99%) (Fig 2). No differences in osseointegra-tion were observed in patients with or without osteo-porosis, regardless of antiresorptive therapy.

For analysis of osseointegration in diabetic patients, a total of 360 studies were identified, with an addi-tional 11 studies also identified. After exclusion and inclusion criteria were assessed, 20 studies remained (Fig 3). Of the 20 studies, 5 were retrospective, 5 were case-control studies, and 10 were prospective studies (Table 4). For diabetic patients, the pooled estimate for implant survival rate from the meta-analysis was 98% (with a 95% CI of 96%–99%) (Fig 4). No differences were observed in osseointegration rates in diabetic vs non-diabetic patients.

Table 1 Study Inclusion/Exclusion Criteria

Inclusion criteria Exclusion criteria

Human clinical studies with at least 10 patients Animal or in vitro studies

Published in the English language Published in non-English language

At least a 3-month follow-up interval Lack of follow-up

Reported osseointegration/survival rate No information on survival/osseointegration status

Patients with specific systemic illnesses or medications –

Records after duplicates removed (n = 323)

Additional records identified (n = 14)

Records identified through the database searching

(n = 394)


Full articles assessed for eligibility (n = 66)


Studies included in quantitative synthesis (n = 21)

Studies included for quantitative assessment for patients taking ARs

(n = 17)

Studies included for quantitative assessment

for patients not taking ARs (n = 4)

Fig 1 Search strategy for osteoporosis articles. Flowchart de-picting articles identified, screened, and included in qualitative and quantitative analysis. ARs = antiresorptives.

http://www.r-project.org


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There were an inadequate number of studies to conduct a meta-analysis for patients with neurologic disorders, HIV, cardiovascular disease, RA, selective serotonin receptor inhibitors, or PPIs. However, evalu-ation of implant survival was performed from available studies. Only one study that was not a case report was available on patients with neurologic disorders. Of the 27 patients with 70 implants in this study, the implant survival rate was 86%.45 Although few studies are avail-able on patients with HIV, two studies demonstrated only 1 implant lost out of 135, for a 99.2% survival rate compared to 100% survival in HIV-negative patients with 24 implants.46,47 For patients with cardiovascular disease, no differences were found in implant survival rate as compared to controls, where one study dem-onstrated 97.8% vs 100%42 and another demonstrated 87.3% vs 87%.48 In 56 patients with RA, high survival

rates between 96.1% to 100% were achieved in 215 implants from two retrospective studies.49,50 For hypo-thyroidism, two studies were found that demonstrated similar implant survival between healthy and disease patients.51,52

In addition to systemic diseases, certain medica-tions known to affect bone formation and remodeling were evaluated. The use of antihypertensive medica-tions for cardiovascular disease was associated with increased implant survival of 99.4% vs 95.9% in pa-tients not taking these medications.53 In contrast, patients taking SSRIs demonstrated a lower implant survival rate of 89.4% to 94.4% vs 95.4% to 98.15% in control patients, respectively.53,54 Similarly, patients taking PPIs had a decreased implant survival rate of 88% to 93.2% vs 95.5% to 96.8% in PPI naïve patients, respectively.53,55

Table 2 Studies on Implants in Osteoporosis Patients Not Taking Bisphosphonates Included in the Analysis

Study (year) Design

Osteoporosis patients Control group

No. of implants % Osseointegrated No. of implants % Osseointegrated

Chow et al101 (2017) PS 158 97 – –

Alsaadi et al35 (2007) PS 29 100 677 97.97

Amorim et al26 (2007) CC 39 97.4 43 100

Von Wowern and Gotfredsen102 (2001) CC 14 100 22 100

PS = prospective study; CC = case control study.

Table 3 Studies on Implants in Osteoporosis Patients Taking Bisphosphonates Included in the Analysis

Study (year) Design

Osteoporosis patients Control group

No. of implants % Osseointegrated No. of implants % Osseointegrated

Siebert et al103 (2015) PS 60 97 12 100

Famili et al104 (2011) RS 75 98.7 – –

Kasai et al105 (2009) RS 35 85.7 167 95.7

Memon et al106 (2012) RS 1,533 93.5 132 95.5

Zahid et al72 (2011) RS 51 94.1 661 97.1

Dvorak et al40 (2011) RS 46 87 – –

Jeffcoat67 (2006) CC 102 100 108 99.2

Famili and Zavoral107 (2015) PS 20 100 – –

Bell and Bell108 (2008) RS 101 95 – –

Fugazzotto et al68 (2007) PS 169 100 – –

Shabestari et al109 (2010) RS 46 100 – –

Tallarico et al110 (2016) PS 98 98 – –

Yajima et al111 (2017) CC 25 88.9 28 100

Temmerman et al112 (2017) CC 63 98.4 63 100

Grant et al70 (2008) RS 468 99.6 1,450 99

Koka et al113 (2010) RS 121 99.17 166 98

Mozzati et al114 (2015) RS 1,267 98.7 – –

PS = prospective study; RS = retrospective study; CC = case-control study.


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DISCUSSION

Dental implants are a first-line therapy to replace sin-gle and multiple missing teeth, with many studies doc-umenting short- and long-term survival rates above 95%.56 As implants become more successful, patients with systemic medical problems are seeking treatment with increased frequency. Although health conditions and medications are known to affect general wound

References Survived TotalSurvival

rate 95% CI

Patients on bisphosphonates

Siebert et al (2015) 120 120 1.00 [0.97; 1.00]

Famili et al (2011) 342 347 0.99 [0.97; 1.00]

Zahid et al (2011) 48 51 0.94 [0.84; 0.99]

Jeffcoat (2006) 26 26 1.00 [0.87; 1.00]

Famili and Zavoral (2015) 20 20 1.00 [0.83; 1.00]

Bell and Bell (2008) 96 101 0.95 [0.89; 0.98]

Shabestari et al (2010) 46 46 1.00 [0.92; 1.00]

Grant et al (2008) 466 468 1.00 [0.98; 1.00]

Kasai et al (2009) 190 202 0.94 [0.90; 0.97]

Memon et al (2012) 269 285 0.94 [0.91; 0.97]

Dvorak et al (2011) 153 177 0.86 [0.80; 0.91]

Fugazzotto et al (2007) 169 169 1.00 [0.98; 1.00]

Tallarico et al (2016) 212 214 0.99 [0.97; 1.00]

Yajima et al (2017) 52 53 0.98 [0.90; 1.00]

Temmerman et al (2017) 144 145 0.99 [0.96; 1.00]

Koka et al (2010) 283 287 0.99 [0.96; 1.00]

Mozzati et al (2015) 1,251 1,267 0.99 [0.98; 0.99]

Subtotal (I2 = 85%, P < .01) 3,978 0.98 [0.97; 0.99]

Patients on other drugs

Wu et al (2016) 1,490 1,499 0.99 [0.99; 1.00]

Wu et al (2014) 84 94 0.89 [0.81; 0.95]

Altay et al (2018) 103 109 0.94 [0.88; 0.98]

Wu et al (2017) 124 133 0.93 [0.88; 0.97]

Chrcanovic et al (2017) 220 250 0.88 [0.83; 0.92]

Subtotal (I2 = 94%, P < .01) 2,085 0.95 [0.88; 0.98]

Pooled estimate 6,063 0.98 [0.96; 0.99]

0.80 0.85 0.90 0.95 1.0Survival rate

Fig 2 (Above) Forest plot of implant survival rate for osteopo-rotic patients.

Fig 3 (Right) Search strategy for diabetes articles. Flowchart depicting articles identified, screened, and included in qualita-tive and quantitative analysis.

Records after duplicates removed (n = 353)

Additional records identified (n = 11)

Records identified through the database searching

(n = 360)


Full articles assessed for eligibility (n = 53)


Studies included in quantitative synthesis (n = 20)


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healing, compromise the immune system, decrease vascularity, and inhibit bone remodeling, the direct effect on implant osseointegration is not well under-stood, or well-studied. The focus of this review was to evaluate the available literature for evidence of sys-temic health conditions and medications that affect implant osseointegration. Even though the search identified studies on osteoporosis, diabetes, neuro-cognitive diseases, cardiovascular disease, HIV, RA, and hypothyroidism, and medications such as SSRIs, PPIs, and antihypertensives, only diabetes and osteoporosis had enough data to support a systematic review.

As previous studies have identified both diabetes and osteoporosis to have a negative effect on implant surviv-al, more recent papers have failed to confirm these initial findings.13,33,57 Whether it is due to newer enhanced im-plant surfaces, improved disease control, or some other external factors, implants in patients with diabetes or osteoporosis appear to be a safe and effective treatment option.22,33 One caveat in diabetic studies is disease control, where poorly controlled diabetics were either excluded or lacked adequate subject numbers to draw definitive conclusions from most studies. In addition, even though initial implant osseointegration is achieved

between 3 and 6 months, long-term survival or success may still be compromised in diabetic patients, with an increased incidence of peri-implantitis.21,22,58

Dental implant failures, classically defined as early or late, can occur due to a variety of reasons.59,60 Early failures generally occur when implants fail to osseoin-tegrate, often due to infection; late failures occur fol-lowing loading and can be due to a variety of different factors, including retained cement, occlusal loading, and peri-implantitis. While studies identified in this review were often long-term studies, data for osseo-integration status at loading was available and utilized for analysis. Consequently, many studies reported late failures 1 to 2 years following placement, after the im-plant was restored with a dental prosthesis.

DiabetesDiabetes, a systemic metabolic disorder, is character-ized by high blood glucose levels. In chronic states, pro-inflammatory cytokines and mediators, such as TNF-alpha and IL-6, become locally increased and can reduce osteoblast-osteoclast coupling, two vital cell types needed for implant osseointegration.61 Simi-larly, diabetes affects the ratio of receptor activator

Table 4 Studies on Implants in Diabetic Patients Included in the Analysis

Study (year) Design

Diabetic patients Control group

Type of diabetes

No. of implants

% Osseointegrated

No. of implants

% Osseointegrated

Turkyilmaz115 (2010) RS WC 23 100 – –

Erdogan et al 116 (2015) PS WC 22 100 21 100

Dowell et al63 (2007) CC Varying levels of control

39 100 11 100

Eskow and Oates57 (2017) PS PC 70 98.6 – –

Oates et al117 (2014) CC Varying levels of control

134 99.2 100 99

Aguilar-Salvatierra et al118 (2016) PS Varying levels of control

52 98.1 – –

Farzad et al119 (2002) RS WC 136 96.3 – –

Al Amri et al120 (2017) PS WC 108 100 – –

Cabrera Domínguez et al121 (2017) PS WC 15 100 15 100

Khandelwal et al122 (2013) PS WC 48 100 – –

Peled et al123 (2003) PS WC 141 100 – –

Abdulwassie and Dhanrajani124 (2002) PS WC 113 95.6

Morris et al125 (2000) RS WC 255 92.2 2,632 93.2

Olson et al126 (2000) PS WC 178 93.3

Fiorellini et al127 (2000) RS WC 215 88.8

Kapur et al128 (1998) PS WC 104 100

Anner et al129 (2010) RS WC 177 97.2 1,449 95

Ghiraldini et al130 (2016) CC WC 31 100 19 100

Tawil et al131 (2008) CC WC 255 97.6 244 99.6

Gómez-Moreno et al132 (2015) CC WC 46 100 21 100

PS = prospective study; RS = retrospective study; CC = case-control study; PC = poorly controlled; WC = well-controlled.


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of nuclear factor kappa-B ligand (RANKL) and osteo-protegrin (OPG), master regulators of osteoclast func-tion. In times of hyperglycemia, this ratio is disrupted and favors increased bone resorption.61 Finally, these patients are more susceptible to both systemic and localized infections, and thus are at a risk for osseoin-tegration failure due to infection.62 Altogether, these changes could be a reason for a potential increase in implant failure in diabetic patients.

Importantly, diabetic control is evaluated by measur-ing the levels of glycosylated hemoglobin, hemoglobin A1c (HbA1c). Indeed, dental precautions exist for pa-tients with uncontrolled diabetes, and most practitio-ners limit their placement of dental implants, as well as elective surgical procedures, in patients with uncon-trolled diabetes. As such, a majority of studies identified included patients with controlled diabetes (HbA1c < 8). Only two studies were identified comparing implant survival in patients with different levels of HbA1c. One study investigated osseointegration rates in patients with uncontrolled diabetes and found no differences in patients with high or low HbA1c.63 Another study that included 23 patients with HbA1c from 6 to 13.9 who had a total of 70 implants placed found no difference in

osseointegration among groups.57 In both studies, im-plant survival rates were comparable, with no statistical differences among groups. However, with low sample sizes, it is difficult to formulate definite conclusions for implants in patients with poorly controlled diabetes.

Indeed, systematic reviews have been conducted for diabetes and dental implant therapy with varied conclusions. One review states that implant osseo-integration is delayed in diabetic patients based on two separate reports that investigate resonance fre-quency analysis (RFA) values during the osseointegra-tion period. In these studies, RFA values are lower in diabetic patients, and patients with poor glycemic control led to delays in implant stabilization. How-ever, survival rates in one study are unreported; in the other, successful osseointegration is reported at 99.2% in diabetic patients and 99% in non-diabetic patients. While there may be a delayed healing pro-cess in diabetics, successful osseointegration seems to be achieved. These results are supported by long-term implant survival where no difference in implant survival is noted.22,58 However, when evaluating bone changes, both studies report significantly higher mar-ginal bone loss in patients with diabetes.

References Survived TotalSurvival

rate 95% CI

Turkyilmaz (2010) 23 23 1.00 [0.85; 1.00]

Erdogan et al (2015) 22 22 1.00 [0.85; 1.00]

Dowell et al (2007) 39 39 1.00 [0.91; 1.00]

Eskow and Oates (2017) 69 70 0.99 [0.92; 1.00]

Oates et al (2014) 218 220 0.99 [0.97; 1.00]

Aguilar-Salvatierra et al (2016) 51 52 0.98 [0.90; 1.00]

Farzad et al (2002) 131 136 0.96 [0.92; 0.99]

Al Amri et al (2017) 108 108 1.00 [0.97; 1.00]

Cabrera Domínguez et al (2017) 15 15 1.00 [0.78; 1.00]

Khandelwal et al (2013) 47 48 0.98 [0.89; 1.00]

Peled et al (2003) 137 141 0.97 [0.93; 0.99]

Abdulwassie and Dhanrajani (2002) 108 113 0.96 [0.90; 0.99]

Morris et al (2000) 235 255 0.92 [0.88; 0.95]

Olson et al (2000) 166 178 0.93 [0.89; 0.96]

Fiorellini et al (2000) 191 215 0.89 [0.84; 0.93]

Kapur et al (1998) 104 104 1.00 [0.97; 1.00]

Anner et al (2010) 172 177 0.97 [0.94; 0.99]

Ghiraldini et al (2016) 31 31 1.00 [0.89; 1.00]

Tawil et al (2008) 249 255 0.98 [0.95; 0.99]

Gómez-Moreno et al (2015) 46 46 1.00 [0.92; 1.00]

Pooled estimate (I2 = 60%, P < .01) 2,248 0.98 [0.96; 0.99]

0.80 0.85 0.90 0.95 1.0Survival rate

Fig 4 Forest plot of implant survival rate for diabetic patients.


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In the present review, osseointegration was mea-sured at the time of definitive loading, which was at least 3 months after surgical implant placement. In the studies included, no differences were observed between implant osseointegration at 3 months in dia-betic and non-diabetic patients. While a vast majority of studies included patients with controlled diabetes, two studies were found that directly compared healthy patients and diabetic patients with both poorly con-trolled and well-controlled disease. Osseointegration rates in all groups were similar, with no statistically sig-nificant differences. In fact, implant survival rates were 97% (95% CI: 94%–98%) in diabetic patients. Although these results are encouraging for osseointegration and short-term implant survival, marginal bone loss and long-term implant success may be affected by diabetic status; this highlights the need to closely follow these patients for maintenance and potential complications.

OsteoporosisSince osteoporosis is a disease of decreased bone mass, increased bone fragility, and susceptibility to fracture, the thin cortical bone and increased tra-becular spacing is thought to contribute to higher implant failure. This has been shown in previous stud-ies of postmenopausal women on hormone replace-ment therapy, especially in the less dense maxillary (type IV) bone.14,34 However, a recent systematic re-view of 10 studies where both osteoporotic and non-osteoporotic patients were compared (702 implants in 188 osteoporotic patients and 4,114 implants in 348 healthy patients), failure rates were similar, with 4.70% vs 3.57% at the implant level and 5.85% vs 4.89% at the patient level, respectively (P > .05).33 Similarly, our me-ta-analysis revealed a 97% implant survival rate (95% CI: 94%–98%) in osteoporotic patients.

Osteoporosis therapy most commonly includes an antiresorptive medication such as a bisphosphonate (BP) or denosumab (Dmab). Both BPs and Dmab in-hibit osteoclast differentiation and function, leading to reduced bone resorption and remodeling.64,65 Since millions of prescriptions for antiresorptives are written for osteoporosis, patients and clinicians are concerned about how these medications affect implant osseoin-tegration. Although most studies have failed to show a negative effect on implant osseointegration or sur-vival after BP therapy, osteonecrosis of the jaw (ONJ) and alveolar bone loss have been reported after den-tal implant placement.66–74 Moreover, patients with previously osseointegrated implants can develop ONJ after antiresorptive therapy is initiated.75 With the po-tentially devastating consequences of ONJ, accepted treatment guidelines should be followed, including avoiding elective placement of dental implants in pa-tients on antiresorptive medications for malignancies,

and utilizing a drug holiday and a thorough informed consent in patients on antiresorptives for osteoporo-sis.76 This controversy in the literature has left patients and dentists with questions regarding implant osseo-integration in osteoporotic patients, effects of medica-tions such as BPs on implant survival, and risk of ONJ in patients treated for osteoporosis.

Human Immunodeficiency Virus The life expectancy for people living with HIV has sig-nificantly improved over the past two decades. Many people who are HIV-positive now live much longer, healthier lives. In fact, the most common causes of ill-ness and death in people living with HIV are now simi-lar to those of non-HIV patients. They include heart disease, kidney disease, liver disease, diabetes, depres-sion, and cancer. The development of highly active antiretroviral therapy (HAART) in the mid 1990s has not only drastically reduced the mortality rates of HIV-positive patients, but also lessened patient morbidity and transmission of the disease. Nonetheless, there is a positive correlation between HIV-positive individuals and bone metabolic alterations. Reasons for this are low calcium/vitamin D intake, low testosterone, alco-hol and opiate abuse, smoking, depression, physical inactivity, and HAART.77 To date, there is little evidence of the effect of HIV, and more specifically HAART ther-apy, on osseointegration and long-term success and survival of dental implants. In a pilot study, Oliveira and coworkers47 aimed to determine the success rate of dental implants placed in patients who were posi-tive for HIV and were receiving different regimens of HAART. The authors assessed the peri-implant health of 59 implants 6 and 12 months after loading. They ana-lyzed success in relation to CD4+ cell counts, viral load, and baseline pyridinoline and deoxypyridinoline val-ues. Finding the higher baseline levels of pyridinoline and deoxypyridinoline in HIV-positive participants did not interfere with osseointegration after 12 months of follow-up, they concluded that the placement of dental implants in HIV-positive patients is a reasonable treat-ment option, regardless of CD4+ cell count, viral load levels, and type of antiretroviral therapy. In a systemat-ic review, Ata-Ali and colleagues78 analyzed the impact of HIV infection on dental implant osseointegration. Nine studies finally met the inclusion criteria and were selected for inclusion in the systematic review. A total of 173 dental implants were placed in 80 patients (135 implants in 56 HIV-positive and 38 implants in 24 HIV-negative patients), and a single loss of dental implant osseointegration was recorded in one HIV-positive pa-tient. The results of the systematic review suggested that dental implant placement in HIV-positive patients did not increase dental implant failure. Nonetheless, the authors concluded that prophylactic antibiotic


Aghaloo et al

treatment, the administration of highly active antiret-roviral therapy, and control of the CD4+ T lymphocyte counts were key in the successful treatment of these groups of patients.

Cardiovascular Disease and Antihypertensive MedicationsAccording to the Centers for Disease Control and Pre-vention, about 610,000 people die of cardiovascular disease in the United States every year, or one in every four deaths. In fact, heart disease is the leading cause of death for both men and women, and coronary heart disease is the most common type, killing over 370,000 people annually.5 Of the different forms of cardiovas-cular disease, hypertension, atherosclerosis, vascular stenosis, coronary artery disease, and congestive heart failure seem to have the most direct effect on periph-eral blood supply. These manifestations translate into lack of oxygen supply to local tissues, decreasing fibro-blast activity, collagen synthesis, capillary growth, and macrophage activity.79 It would therefore be expected to have a direct influence on the process of bone heal-ing and osseointegration following dental implant sur-gery. In a retrospective analysis of 246 consecutively treated patients, Khadivi et al48 analyzed the relation-ship between cardiovascular disease and risk for im-plant failure. The patients comprised a cardiovascular disease interest group of 39 patients and control sub-groups of 98 healthy and 109 patients with a history of other systemic disease. Differences in implant failure rates between the groups were not found to be sta-tistically significant. Though the sample size was small, the authors concluded that cardiovascular disease may not be a risk factor for successful osseointegration. In a retrospective study analyzing nearly 7,000 implants, Alsaadi and coworkers analyzed the impact of local and systemic factors on the incidence of oral implant failures, up to abutment connection.42 They concluded that certain factors, such as cardiac diseases, coagula-tion problems, hypertension, or hypercholesterolemia, did not lead to an increased incidence of early failures. In a review paper by Diz and coworkers,80 the success and survival rates of dental implants were observed in medically compromised patients. The authors could find no evidence of cardiac disorders as contraindica-tions to dental implants. The authors highlighted the importance of considering other issues, such as the occurrence of bleeding or cardiac ischemic events dur-ing surgery, and recommended that consultation with a cardiologist be arranged prior to surgery. When con-sidering the favorable effect of antihypertensive drugs on bone formation and remodeling and decreased risk of bone fractures, the possible positive effect on dental implant osseointegration is intriguing. A recent retrospective cohort study with 1,499 implants in 728

patients (327 implants in 142 patients taking antihy-pertensive medications and 1,172 implants in 586 healthy patients) demonstrated a lower implant fail-ure rate (0.6%) in medicated patients vs 4.1% failure in healthy patients.53

Neurologic DisordersAccording to the American Neurological Association, neurologic diseases impact an estimated 100 mil-lion Americans every year. The most common neuro-logic diseases cost the United States $789 billion in 2014, and this figure is projected to grow, as the el-derly population is estimated to double by 2050. His-torically, patients suffering from neurologic diseases have been excluded from receiving dental implants. The main reason for the exclusion has been the asso-ciation with poor access to oral health care, poor oral hygiene, oral parafunctions such as bruxism, harmful habits, and behavioral problems. Treatment of eden-tulism with removable dentures may present a chal-lenge for these patients due to the aforementioned reasons. Edentulism can lead to deficient nutritional intake, dietary enjoyment, self-esteem, social inter-action, and social acceptability. These problems are likely to aggravate existing difficulties during eating and swallowing.

Modern technology and advances in medicine have allowed for vast improvements in patient care and quality of life, including patients suffering from neurologic disorders. Advances in pain and symptom management, access to hospice and assisted care, and emotional and spiritual assistance have allowed these patients to regain a sense of normalcy. This improve-ment in patients’ psychological and social well-being has been a decisive factor in the reintroduction of dental implants, allowing them to function in a similar manner to their natural teeth. Unfortunately, there is little evidence to support the use of dental implants in patients affected by neurologic disorders. Two reports evaluated the use of osseointegrated dental implants in patients with Parkinson’s disease. In the first one, Chu et al describe the use of a magnetic attachment system in an implant-supported mandibular overden-ture for an edentulous patient.81 After the implant osseointegrated, the patient was followed during a 12-month maintenance period. The authors recorded looseness of one magnet on one occasion. This did not recur after retightening. The patient was satisfied with the functional improvement achieved by the prostheses, and family members noted an improve-ment in the patient’s selection of foods. Heckmann et al described a follow-up period of up to 42 months on three patients rehabilitated with implant-assisted mandibular overdentures retained by non-rigid tele-scopic attachments.82 No implants were lost during


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their observation period, and the patients reported re-markable improvement in their chewing ability. Both studies agreed that dental implants appear to be a useful adjunctive treatment in edentulous Parkinson’s disease patients and may be considered for patients with diseases similarly affecting motor skills.

In a prospective study, Ekfeldt et al evaluated the medium- to long-term outcome of dental implant therapy in patients with neurologic disabilities.45 Twenty-seven patients with different disabilities and in need of prosthodontic treatment were treated with various implant-supported prostheses. Altogether, 88 threaded titanium implants were placed, of which 70 were available for examination at the final follow-up. A total of 12 implants (14%) were lost, 3 before load-ing and 9 after insertion of the implant-supported fixed prostheses. The cumulative survival rate for im-plants placed was 85.8% after 10 years. Peri-implant mucositis was diagnosed in 10 patients and for 14 of the 70 implants. Three of the 15 patients with measur-able radiographs and 4 implants were diagnosed with peri-implantitis. The studies also looked at prosth-odontic aspects of the treatment. Prosthetic compli-cations occurred, from minor and easily correctable to more severe and requiring retreatment. Nonetheless, the authors concluded that implant therapy can be a valid option for the rehabilitation of patients with neurologic disabilities.

HypothyroidismHypothyroidism is a common endocrine disorder, with an increased prevalence in women and in advanced age. Since most organs have receptors for thyroid hormone, its deficiency interferes with many of the body’s metabolic processes. In addition to regulating temperature, generalized energy, metabolism, skin moisture, gastrointestinal motility, muscle metabo-lism, mental and memory ability, libido, and menstrual cycle, thyroid hormone affects bone metabolism.51,83 Thyroid hormone helps maintain adult bone mass and stimulates production of insulin-like growth factor-1 (IGF-1), which increases osteoblast formation and dif-ferentiation, and bone remodeling.84–86 Specifically for bone metabolism, hypothyroidism has been as-sociated with delayed bone regeneration, increased fracture risk, and delayed fracture repair.87,88 Treat-ment for hypothyroidism, including long-term levo-thyroxine, has also been associated with increased risk for osteoporosis and delayed fracture recovery in animal studies,89 making the condition and its ther-apy a cause for concern in patients seeking dental implants. Studies that have investigated implant sur-vival in patients with hypothyroidism did not demon-strate a significantly higher rate of implant failures as compared to control patients.52,90

Rheumatoid ArthritisRA is an autoimmune disease in which the body’s im-mune system creates inflammation that causes the synovium to thicken, resulting in edema and pain in and around the joints, eventually affecting the bone itself. RA is frequently accompanied by osteoporosis as a result of increased systemic bone turnover and anti-inflammatory and/or combined anti-immune treatment regimens. Approximately 1.5 million people in the United States have RA and nearly three times as many women as men have the disease. Although the cause of RA is not yet fully understood, there is evidence that genetics, hormones, and environmental factors are involved in the process. Researchers have shown that people with a specific HLA shared epitope have a fivefold greater chance of developing RA than those without the marker. Other genes connected to RA include: STAT4, a gene that plays important roles in the regulation and activation of the immune system; TRAF1 and C5, two genes relevant to chronic inflam-mation; and PTPN22, a gene associated with both the development and progression of RA. Researchers con-tinue to investigate other factors that may play a role. Some include exposure to cigarette smoke, air pollu-tion, insecticides, and occupational exposures to min-eral oil and silica. It is well known that patients with RA with or without concomitant corticosteroid treatment will develop localized osteopenia and generalized os-teoporosis in 30% to 50% of all cases.91 Despite this, there is little evidence on the effect of RA on osseoin-tegration and dental implant outcomes.

In a case series, Weinlander et al evaluated implant and prosthodontic treatment outcomes of patients suffering from rheumatic disorders such as RA and connective tissue diseases (CTDs).49 Overall, 89 im-plants were inserted for rehabilitations such as single-tooth replacement (n = 8), fixed partial dentures (n = 14), complete dentures (n = 5), and overdentures (n = 2). The mean evaluation period was 42.6 months. The evaluation focused on the cumulative implant survival and success rates and peri-implant condi-tions, as well as incidence and type of prosthodontic complications. The authors reported a high implant survival rate during follow-up, with a cumulative 3-year implant success rate of 96.1%. Patients with RA demonstrated acceptable marginal bone resorption (mean: 2.1 ± 0.5 mm) and good soft tissue conditions, while CTD patients showed increased bone resorp-tion (mean: 3.1 ± 0.7 mm). This was especially noted in scleroderma patients, as were major peri-implant soft tissue alterations (Bleeding Index) in patients suffer-ing from Sjogren’s syndrome. The authors concluded that a high implant and prosthodontic success rate can be anticipated, even for patients suffering from auto-immune rheumatic disorders such as RA and CTDs.


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Nonetheless, the authors also encourage a rigorous maintenance program, including optimal oral hygiene, to assist in ensuring stable long-term results for CTD patients with more vulnerable soft tissue conditions. These results were confirmed in another study of 34 patients with RA, demonstrating 100% implant surviv-al and 93.8% success after 3.5 years, which was similar even in the presence of concomitant connective tissue disease. However, peri-implant soft tissue alterations and some bone resorption were seen in patients with combined RA and CTDs.50

Selective Serotonin Reuptake InhibitorsIntroduced in 1988, SSRIs are the most commonly pre-scribed antidepressants, increasing levels of serotonin in the brain. They accomplish that by blocking the re-absorption (reuptake) of serotonin in the brain, making more serotonin available. They act by preventing the reuptake of 5-hydroxytryptamine (5-HT) (serotonin) through the inhibition of the 5-HT transporter (5-HTT) located on the presynaptic neuron, thereby increasing levels of 5-HT within the synaptic cleft and modulating neurochemical signaling, releasing 5-HT directly.

SSRIs have a broad range of indications, including depression, generalized anxiety disorder, panic dis-order, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, premenstrual dysphoric disorder, and bulimia nervosa.92 Recent investigations have also shown a wider role for the SSRIs, as 5-HT plays an active role in numerous path-ways including that of bone metabolism.93 Osteoblasts and osteoclasts express 5-HT receptors and can be ex-posed to 5-HT via autocrine, paracrine, and endocrine pathways. Clinical studies have shown a relationship between the use of SSRIs, reduced bone mineral den-sity, and an increased risk of bone fracture.94–96 Despite the mounting evidence of the effect of SSRIs on bone and bone metabolism, little evidence exists of their ef-fect on the process of osseointegration and ultimately, dental implant outcomes.

In a retrospective cohort study conducted by Wu et al, 94 implants placed in 51 patients using SSRIs were evaluated to estimate the risk of failure associated with the use of SSRIs.97 After 3 to 67 months of follow-up, 10 implants failed and 84 survived in the SSRI-users group. SSRI usage was associated with an increased risk of dental implant failure (hazard ratio, 6.28; 95% CI = 1.25–31.61; P = .03). The failure rates were 4.6% for SSRI nonusers and 10.6% for SSRI users. Their findings indi-cated that treatment with SSRIs was associated with an increased failure risk of osseointegrated implants, and they went on to suggest a careful surgical treatment planning for SSRI users.

Altay and coworkers conducted a retrospective cohort study to investigate the association between

systemic intake of SSRIs and failure of osseointegra-tion in patients rehabilitated with dental implants.98 A total of 2,055 osseointegrated dental implants in 631 patients (109 implants in 36 SSRI users and 1,946 in 595 nonusers) were evaluated. Median duration of follow-up was 21.5 (range, 4–56) months for SSRI users and 23 (range, 3–60) months for nonusers (P = .158). The failure rate was 5.6% for the SSRI users and 1.85% for the nonusers. The difference between the two groups failed to reach statistical significance at patient and im-plant levels (P = .166 and P = .149, respectively). The odds of implant failure were 3.123 times greater for SSRI users compared to nonusers. Patients using SSRIs were found to be 3.005 times more likely to experience early implant failure than nonusers. The results of this study suggested that SSRIs may lead to an increase in the rate of osseointegration failure, although not reaching statistical significance.

Proton Pump InhibitorsPPIs are among the most commonly used drugs in the world. About 15 million people in the United States use PPIs every year. They are used for the prevention and treatment of acid-related conditions such as esopha-geal, duodenal, and stomach ulcers; NSAID-associated ulcer; gastroesophageal reflux disease (GERD); and Zollinger-Ellison syndrome. They also are used in com-bination with antibiotics for eradicating Helicobacter pylori, a bacterium that together with acid causes ul-cers of the stomach and duodenum. Chronic use of PPIs could have potential detrimental effects, mainly due to the effect of chronic acid suppression on the ab-sorption of vitamins and nutrients. Gastric acid secre-tion can affect the absorption of a number of nutrients, drugs, and vitamins, particularly Vitamin B12, iron, cal-cium, and magnesium. Recent studies showed a pos-sible association between chronic PPI use and increase in bone fractures, possibly by decreasing calcium ab-sorption.99 The most widely assumed mechanism is that long-term PPI use leads to decreased intestinal absorption of calcium, resulting in negative calcium balance, increased osteoporosis, development of sec-ondary hyperparathyroidism, increased bone loss, and increased fractures. Even though evidence exists on the negative effects of PPIs on bone, there is little evi-dence on their effect on osseointegration and dental implants.

In a retrospective cohort study, Wu et al investi-gated the association between PPIs and the risk of os-seointegrated implant failure.100 The analysis included a total of 1,773 dental implants in 799 patients (133 implants in 58 PPIs users and 1,640 in 741 nonusers). Statistical analysis revealed that the failure rates were 6.8% for people using PPIs compared to 3.2% for non-users. Subjects using PPIs had a higher risk of dental


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implant failure (hazard ratio: 2.73; 95% CI = 1.10–6.78) compared to those who did not use the drugs. The findings suggested that treatment with PPIs may be associated with an increased risk of osseointegrated dental implant failure. Chrcanovic and coworkers con-ducted a retrospective cohort study based on patients consecutively treated between 1980 and 2014 with implant-supported/retained prostheses.55 A total of 3,559 implants were placed in 999 patients, with 178 implants reported as failures. The implant failure rates were 12.0% (30/250) for PPI users and 4.5% (148/3,309) for nonusers. A total of 45 out of 178 (25.3%) failed im-plants were lost up to abutment connection (6 in PPI users, 39 in nonusers), with an early-to-late failure ra-tio of 0.34:1. The intake of PPIs was shown to have a statistically significant negative effect for implant sur-vival rate (hazard ratio = 2.811; 95% CI = 1.139–6.937; P = .025). In their conclusions, the authors also sug-gested that the intake of PPIs may be associated with an increased risk of dental implant failure.

CONCLUSIONS

Altogether, there is not sufficient data to conclude that diabetes affects the ability of dental implants to osseointegrate. However, most diabetes studies focus on well-controlled disease, especially as measured by HbA1C. Long-term studies evaluating dental im-plants in diabetic patients do report an increase in marginal bone loss and peri-implantitis in these pa-tients, clearly differentiating the role of diabetes in the osseointegration process vs bone remodeling or soft tissue responses over the long term. This review also failed to uncover direct evidence that dental implant osseointegration is impaired in patients with osteopo-rosis. Although the disease itself has no direct effect, osteoporosis is generally treated with antiresorptive medications that can increase the risk of osteonecro-sis of the jaws. This is a devastating complication that is widely understudied in the literature, either from the lack of elective implant surgical procedures per-formed in cancer patients receiving antiresorptives, or the insufficient patient numbers included in studies of osteoporosis patients receiving antiresorptives, to ad-equately assess ONJ risk.

In addition, there is no direct evidence that patients with HIV, cardiovascular disease, neurologic disorders, hypothyroidism, or RA have a decreased rate of im-plant osseointegration. However, some preliminary evidence suggests that medications such as SSRIs or PPIs may have a negative effect on implant osseointe-gration. These studies are fairly recent and lacked the body of literature necessary to perform a systematic re-view, and therefore must be validated with continuous

research. Moreover, disease control, concomitant med-ications, and other comorbidities complicate implant osseointegration, and must guide our treatment ap-proaches and clinical guidelines.

DISCLOSURE

The authors have no conflicts of interest related to this study.

ACKNOWLEDGMENTS

The authors would like to express their gratitude for the hard work and input from the participants in Group 1 of the Acad-emy of Osseointegration Summit in Oakbrook, Illinois on August 9–10, 2018. Participating members included: Gustavo Avila-Ortiz, DDS, MS, PhD; Nadim Baba, DMD, MSD; Ting-Ling Chang, DDS; Steven Eckert, DDS, MS; Robert Eskow, DMD; Brett Fergu-son, DDS; Henry Ferguson, DMD; Jeffrey Ganeles, DMD; Wendy Croll Halpern, DMD; Andrea Henderson, DDS; Fouad Khoury, DMD, PhD; Joerg Neugebauer, DDS, PhD; Robert Taft, DDS; Faleh Tamimi, BDS, PhD; James Taylor, DMD, MA; and our group expert orthopedic surgeon, Susan Bukata, MD. These members contributed significantly to the refinement of the manuscript, in-cluding discussion, critique, and ideas for future studies and re-views. While we could not include all of the excellent input from the group, their ideas have sparked several new projects and future conferences that will continue for many years to come.

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Jeffrey Ackerman, DDS

Tara Aghlaoo, DDS, MD, PhD

Gustavo Avila-Ortiz, DDS, MS, PhD

Nadim Baba, DMD, MSD, FACP

Reva Barewal, DDS, MS

John Brunski, PhD

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