Effect of dental implant surface roughness in patients with a history of periodontal disease: a systematic review and meta-analysis

Abstract Background To review the literature on the effect of dental implant surface roughness in patients with a history of periodontal disease. The present review addresses the following focus question: Is there a difference for implant survival, mean marginal bone loss, and the incidence of bleeding on probing in periodontally compromised patients receiving a machined dental implant or rough surface dental implant? Methods Electronic and manual literature searches were conducted on PubMed/MEDLINE and the Cochrane Library on studies published until May 2018 to collect information about the effect of machined, moderately rough, and rough dental implant surfaces in patients with a history of periodontal disease. The outcome variables implant survival, mean marginal bone level, and the incidence of peri-implantitis and bleeding on probing were evaluated. Meta-analysis was performed to obtain an accurate estimation of the overall, cumulative results. Results Out of 2411 articles, six studies were included in this systematic review. The meta-analysis of the implant survival and implant mean marginal bone loss revealed a risk ratio of 2.92 (CI 95% 0.45, 18.86) for implant failure and a total mean difference of − 0.09 (CI 95% − 0.31, 0.14) for implant mean marginal bone loss measured in a total group of 215 implants, both not statistically significant. Conclusions Due to lack of long-term data (> 5 years), the heterogeneity and variability in study designs and lack of reporting on confounding factors, definitive conclusions on differences in implant survival, and mean marginal bone loss between machined and moderate rough implants in periodontally compromised patients cannot be drawn. Future well-designed long-term randomized controlled trials are necessary to reveal that machined surfaces are superior to moderately rough and rough surfaces in patients with a history of periodontal disease