Prevalence of Peri-implantitis in Medically Compromised Patients and Smokers: A Systematic Review

Turri, Alberto; Rossetti, Paulo Henrique Orlato; Canullo, Luigi; Grusovin, Maria Gabriella; Dahlin, Christer

doi:10.11607/jomi.4149

Cited by 107 publications

(109 citation statements)

References 15 publications

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“…Smoking has been suggested a potential and significant risk factor for adverse implant outcomes . Two systematic reviews showed a significant association between smoking and peri‐implantitis with a prevalence of peri‐implantitis of 36.3% among smokers . Rinke et al .…”

Section: Discussionmentioning

confidence: 99%

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

Atieh

Pang

Lian

et al. 2019

Journal of Periodontology

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Background Further validation of the risk indicators / predictors for peri‐implant diseases is required to allow clinicians and patients to make informed decisions and optimize dental implant treatment outcomes. The aim of this study was to build prediction models, using Chi‐square automatic interaction detection (CHAID) analysis, to determine which systemic‐, patient‐, implant‐, site‐, surgical‐ and prostheses‐related risk indicators had more impact on the onset of peri‐implant diseases. Methods A retrospective analysis of 200 patients who received implant‐supported prostheses between 1998 and 2011 was conducted to evaluate the prevalences and risk indicators for peri‐implant mucositis and peri‐implantitis. The data were further analyzed using CHAID to produce two predictive models. Results The prevalence of peri‐implant mucositis was 20.2% and 10.2% for patients and implants, respectively, while the prevalence of peri‐implantitis was 10.1% at the patient level and 5.4% at the implant level. CHAID decision tree analysis identified three predictors (history of treated periodontitis, absence of regular supportive peri‐implant maintenance, and use of bone graft) for peri‐implant mucositis and three predictors (smoking, absence of regular supportive peri‐implant maintenance, and placement of ≥2 implants) for peri‐implantitis. Conclusions Within the limitations of this study, CHAID decision tree analysis identified the most plausible risk indicators and provided two predictive models for use in a particular university setting that would allow early detection and ensure appropriate care and maintenance of patients at high risk of peri‐implant diseases.

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Section: Discussionmentioning

confidence: 99%

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

Atieh

Pang

Lian

et al. 2019

Journal of Periodontology

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“…A number of risk indicators were identified including poor oral hygiene, a history of periodontitis, diabetes and smoking [2-4]. Peri-implant diseases share similar aetiology and risk factors as chronic periodontitis [5].…”

Section: Introductionmentioning

confidence: 99%

General Diseases Influence on Peri-Implantitis Development: a Systematic Review

Guobis¹,

Pacauskienė²,

Astramskaitė

2016

JOMR

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ObjectivesTo systematically review the influence of systemic diseases or medications used in their treatment on the dental implant therapy success.Material and MethodsThe search strategy was implemented on the National Library of Medicine database (MEDLINE) (Ovid) and EMBASE electronic databases between January 2006 and January 2016. Human studies with available English articles analysing the relationship between dental implant therapy success and systemic diseases, such as diabetes mellitus, AIDS/HIV, rheumatoid arthritis, osteoporosis, Crohn’s disease, cardiovascular diseases, scleroderma, Sjögren’s syndrome, lichen planus, ectodermal dysplasia, post-transplantation status, were included in present review according to the PRISMA guidelines. The review protocol was registered on PROSPERO system with the code CRD42016033662.ResultsPresent review included forty one retrospective and prospective follow-up studies, case-control studies, case report series and cohort studies. Despite some limitations this study reveals positive results of implantation in most systemic conditions that should be interpreted with caution. Influence of cardiovascular diseases on the dental implantation success should be explored deeply, because of controversial results and likelihood of comorbidity expressed by a history of cardiovascular diseases and periodontitis. There is only a weak relationship with bone density in osteoporosis and implant failure. All the other diseases did not show significant effect on implantation success.ConclusionsRecent studies with low strength of evidence and controversy show that systemic diseases may have potential effect on the success of implantation, but further detailed studies are needed to provide these findings.

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“…Nevertheless, an increase in peri‐implantitis has been reported, with frequencies ranging from 14% to 30% 3 . A history of periodontitis, 4 current smoking, and diabetes mellitus (DM) are the most important risk factors associated with peri‐implantitis 5 . However, the importance of dental biofilms in the etiology of peri‐implantitis has been extensively studied as well 6…”

mentioning

confidence: 99%

Microbiome and Microbial Biofilm Profiles of Peri‐Implantitis: A Systematic Review

Lafaurie

Sabogal

Castillo

et al. 2017

Journal of Periodontology

222

166

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Peri-implantitis represents a heterogeneous mixed infection that includes periodontopathic microorganisms, uncultivable asaccharolytic anaerobic Gram-positive rods and other uncultivable Gram-negative rods, and, rarely, opportunistic microorganisms such as enteric rods and Staphylococcus aureus. Sequencing methods that evaluate the entire microbiome improve identification of microorganisms associated with peri-implantitis.

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Prevalence of Peri-implantitis in Medically Compromised Patients and Smokers: A Systematic Review

Cited by 107 publications

References 15 publications

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

General Diseases Influence on Peri-Implantitis Development: a Systematic Review

Microbiome and Microbial Biofilm Profiles of Peri‐Implantitis: A Systematic Review

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