Engineering

Peri

Statistics

Surgery

Soft tissue

Civil engineering

Dentistry

Systematic review

Mathematics

Political science

Implant

Meta-analysis

Internal medicine

Sample size determination

MEDLINE

Grading (engineering)

Medicine

Randomized controlled trial

Clinical Implant Dentistry and Related Research

Background Studies have examined the benefit of having keratinized peri‐implant mucosa width with mixed results. Purpose This study examines whether the lack of a prespecified (2 mm) amount of keratinized mucosa width (KMW) is a risk factor for peri‐implant diseases. Methods A systematic electronic and manual search of randomized or nonrandomized controlled or noncontrolled clinical trials was conducted. Qualitative review, quantitative meta‐analysis, and trial sequence analysis (TSA) of implants inserted at sites with &lt;2 mm or ≥2 mm of KMW were analyzed to compare all the predetermined outcome variables. The level of evidence concerning the role of KMW in peri‐implant health was evaluated via the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system guide. Results Nine studies were included in the qualitative analysis and four in the meta‐analysis and TSA. No significant inter‐group difference ( p &gt; 0.05) and a low power of evidence were found for probing depth, soft‐tissue recession, and marginal bone loss. A significant difference favoring ≥2 mm KMW had a lower mean plaque index (MD = 0.37, 95% CI: [0.16, 0.58], p = 0.002) (3 studies, 430 implants, low‐quality evidence). GRADE system showed very low and low quality of evidence for all other outcome measures. Conclusion Based on the available studies, the impact of amount of KMW (either &lt;2 mm or ≥ 2 mm) as a risk factor for developing peri‐implant disease remains low. Future control studies with proper sample size and longer follow‐up are needed to further validate current findings.

The role of keratinized mucosa width as a risk factor for peri‐implant disease: A systematic review, meta‐analysis, and trial sequential analysis