The Gracilis-Semitendinosus Indeks: A New Intraoperative Predictive Parameter for the Arthroscopic Anterior Cruciate Ligament Hamstring Tendon Reconstruction Durability

• Published in: Orthopaedic journal of sports medicine 2017-02, Vol.5 (2_suppl2)
• Main Authors: Sever, Gokhan Bulent, Budeyri, Aydin, Cankus, Mehmet Cenk, Bostanci, Levent
• Format: Article
• Language: English
• ISSN: 2325-9671; 2325-9671
• DOI: 10.1177/2325967117S00075

Aim: According to the ethnic demographical backgrounds, gracilis(G), and semitendinosus(ST) tendon antropometric properties may vary among the individuals of Turkish and active sporter patient populations. In this study, aimed was to define the G and ST tendon proportional anatomical corellations intraoperatively prior to graft harvesting that are uniquely specific to the Turkish ethnicity. Materials-Methods: The study was begun with 50 active sporting patients who were undergone arthroscopic graft anterior cruciate ligament hamstring tendon reconstruction(aACLHTR) in 2013-2014 at our institution. 15 patients were excluded due to premature graft ruptures, and not meeting the minimum of 4hrs/w active sporting criteria. This retrospective cohort was conducted with a total of 35 knees. Inclusion criteria were; Turkish ethnicity, having been undergone aACLHTR between 2013-2014, having been at an age of 16-50 years, having eligible intraoperative antropometric records, having consistent postoperative follow-ups, having been diagnosed to have complete ACL tear, having no ipsilateral/contralateral, congenital/acquired limb deformities or trauma, surgery or lymphedema history. Exclusion criteria were unwillingness to give written consent, having been a premature graft rupture during harvesting, having insufficient data recordings to meet the eligibility criteria. All the surgeries were performed by one senior surgeon who was blind to the study. All the demographical data, and measurements were recorded by a research assistant who was blind to the study. Pearson correlation coefficients were calculated to detect linear relationship between numerical variables. Beta coefficients were estimated by univariate linear regression analysis. All analysis were performed by using SPSS for windows version 22.0 considering the P