Efficacy of Collagen-Based Membranes in Alveolar Bone Augmentation

Author:

Choi Na-Rae,Sándor George,Kim Yong-Deok

Abstract

Collagen-based membranes (CBMs) have similar permissive bone formation capabilities when compared to non-absorbable membranes. CBMs have been classified as non-cross-linked membranes (NCLMs) and cross-linked membranes (CLMs) depending on whether the cross-linking between the collagen fibers was artificially increased. The purpose of this study is to evaluate the bone maintenance capacity between NCLMs and CLMs by comparing resorption of regenerated bone. The inclusion criteria consisted of: (1) The use of a CBM, either being an NCLM or CLM for coverage of grafted bone; (2) follow-up for more than one year; (3) the presence of the patient's orthopantomographic X-ray (OPTG) immediately following the installation of implants and one year after. The bone resorption observed in the OPTG was determined by measuring the number of exposed threads of the implant. The number of thread exposures in the implant was compared according to CBM types. OPTGs taken immediately following the installation of implants and at one year after installation were compared. The subject of the measurement was always the implant in which the greatest number of exposed threads were present in each patient. A total of 56 subjects and 97 implants were used in this study. There was no significant difference between NCLM and CLM groups (p > 0.05). However, there was a statistically significant difference (p = 0.02) between the groups when a bone graft was applied to both the maxilla and the mandible. The average number of thread exposures was less than 1.5. In this study, no comparison was made between commonly known causes of bone loss and membrane types.

Funder

National Research Foundation of Korea

Korea Health Industry Development Institute

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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