Publication:
Biomechanical Evaluation of the Effect of Arthroscopic Suture Passing Instruments on the Posterior Meniscal Root

Loading...
Thumbnail Image

Date

Journal Title

Journal ISSN

Volume Title

Publisher

Research Projects

Organizational Units

Journal Issue

Abstract

Background: The study aimed to biomechanically evaluate the effect of arthroscopic suture passing instruments used in the treatment of meniscal root tears on the meniscal suture interface in the root region. Methods: A total of 40 intact lateral menisci, obtained during total knee arthroplasty, were procured for the purpose of conducting a biomechanical study. The menisci were randomly assigned to one of two distinct test groups: Group 1 using the Accu-Pass Suture Shuttle (cannulated) and Group 2 using the First-pass Mini Suture Passer (non-cannulated), with each group consisting of n = 20 samples. Maximum failure load, stiffness, and displacement values were obtained using a uniaxial universal tensile testing machine. Results: When the groups were compared in terms of average maximum failure load (Group 1: 152.5N +/- 50.7, Group 2: 162.5N +/- 54.4), no statistically significant difference was observed (P = 0.549). At the moment of maximum failure load, the displacement values of both groups were similar (P = 0.502). In the comparison conducted for both groups in terms of preconditioning and postconditioning stiffness, no significant difference was detected between groups (P-values were 0.252 and 0.210, respectively). Conclusion: In our study, the tissue laceration size created by suture passers at the meniscus-suture interface within the root region was indirectly tested based on the influence of tensile forces. Both suture passers (cannulated and non-cannulated) are similar in terms of maximum failure load, stiffness, and displacement amounts. This study indicates that there is no difference between suture passers for root tears and supports the usability of both methods during surgery. (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Description

Yontar, Onur/0000-0003-0094-7133;

Citation

WoS Q

Q2

Scopus Q

Q2

Source

Knee

Volume

51

Issue

Start Page

93

End Page

101

Endorsement

Review

Supplemented By

Referenced By