Influence of different tibial fixation methods in anterior cruciate ligament (ACL) reconstruction: bone integration, graft maturation, and clinical outcomes.
Primary Objective
To assess whether any of the four tibial fixation techniques is superior in terms of:
Graft-bone integration (CT/MRI),
Graft maturation (MRI),
Clinical outcomes (IKDC, Lysholm, Tegner scores; manual and instrumented knee stability with KT-1000 arthrometer).
Secondary Objectives
Compare clinical outcomes among groups (IKDC, Lysholm, Tegner).
Compare re-rupture rates and patient satisfaction.
Study Design
Prospective randomized trial.
The study will be registered in the international Clinical Trials registry.
Inclusion Criteria
Age ≥18 years.
Complete ACL rupture treated with arthroscopic ACL reconstruction using autologous hamstring tendons.
Exclusion Criteria
Age ≥55 years.
Patients treated with allografts, bone-patellar tendon-bone grafts, or quadriceps tendon grafts.
Malpositioned tunnels (evaluated by MRI at 6 months postop).
Previous knee surgery.
Femoral or tibial deformities.
Multiligament injuries.
Note: Soft tissue procedures (e.g., meniscal repair) are not exclusion criteria.
Randomization
Patients meeting inclusion/exclusion criteria will be randomized (via Research Randomizer: https://www.randomizer.org/
) into 3 groups:
Group A (TORN): bioabsorbable interference screw, same diameter as the tunnel.
Group B (TORN+1): bioabsorbable interference screw, one mm larger than the tunnel diameter.
Group C (TORN-BT): interference screw (same size as tunnel) + second fixation with cortical button/plate.
Sample size: 37 patients per group, 111 total.
Follow-up
Radiographic, CT, and MRI evaluation pre-op, at 15 days, and at 1 year post-op.
Clinical and radiological follow-up for 1 year.
Statistical Analysis
In the prospective phase, Student's t-test will be performed for comparison of pre- and postoperative variables. All variables will also be compared between groups and within groups (baseline vs. end of follow-up) using analysis of variance (ANOVA).
Linear regression models will be applied to test for potential significance that could be masked by confounding factors.