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Effect of Different Membranes on Vertical Bone Regeneration: A Systematic Review and Network Meta-Analysis.

Mi ZhangZili ZhouJiahao YunRui LiuJie LiYimeng ChenHongXin CaiHeng-Bo JiangEui-Seok LeeJian-Min HanYunhan Sun
Published in: BioMed research international (2022)
This study is aimed at performing a systematic review and a network meta-analysis of the effects of several membranes on vertical bone regeneration and clinical complications in guided bone regeneration (GBR) or guided tissue regeneration (GTR). We compared the effects of the following membranes: high-density polytetrafluoroethylene (d-PTFE), expanded polytetrafluoroethylene (e-PTFE), crosslinked collagen membrane (CCM), noncrosslinked collagen membrane (CM), titanium mesh (TM), titanium mesh plus noncrosslinked (TM + CM), titanium mesh plus crosslinked (TM + CCM), titanium-reinforced d-PTFE, titanium-reinforced e-PTFE, polylactic acid (PLA), polyethylene glycol (PEG), and polylactic acid 910 (PLA910). Using the PICOS principles to help determine inclusion criteria, articles are collected using PubMed, Web of Science, and other databases. Assess the risk of deviation and the quality of evidence using the Cochrane Evaluation Manual, and GRADE. 27 articles were finally included. 19 articles were included in a network meta-analysis with vertical bone increment as an outcome measure. The network meta-analysis includes network diagrams, paired-comparison forest diagrams, funnel diagrams, surface under the cumulative ranking curve (SUCRA) diagrams, and sensitivity analysis diagrams. SUCRA indicated that titanium-reinforced d-PTFE exhibited the highest vertical bone increment effect. Meanwhile, we analyzed the complications of 19 studies and found that soft tissue injury and membrane exposure were the most common complications.
Keyphrases
  • bone regeneration
  • high density
  • systematic review
  • soft tissue
  • tissue engineering
  • risk factors
  • stem cells
  • drug delivery
  • body composition
  • case control
  • quality improvement
  • machine learning