Effect of intra-articular tibial tunnel aperture positioning on the clinical outcomes after repair of medial meniscus posterior root tear with pull-out repair technique

Abstract

Background: The impact of tibial tunnel aperture position on outcomes after medial meniscus posterior root (MMPR) repair with a transtibial pull-out technique remains unclear. This study evaluated the association between intra-articular tibial tunnel aperture position and postoperative outcomes after transtibial pull-out MMPR repair using intra-articular assessment. Methods: Patients who underwent arthroscopic MMPR repair between March 2010 and February 2022 were retrospectively reviewed. The tibial tunnel aperture was classified relative to the MMPR footprint midpoint as anterior (Group A; 28 patients) or posterior (Group P; 30 patients). PROMs (VAS, Lysholm score, International Knee Documentation Committee subjective score, and KOOS) and radiologic outcomes (changes in joint space width and Kellgren-Lawrence (K-L) grade progression) were evaluated at >= 2-year follow up. Results: Postoperative outcomes did not differ significantly between groups. However, Group A showed higher rates of K-L grade progression (67.8% vs. 36.7%; P = 0.031) and greater medial joint space narrowing (0.9 mm (interquartile range 0.4-1.4) vs. 0.3 mm (interquartile range 0.0-0.6), P = 0.001) than Group P. Joint space narrowing demonstrated a significant positive correlation with the normalized anteroposterior distance of the tibial tunnel aperture (r = 0.459; P < 0.001),indicating that more anterior tunnels were associated with greater joint space loss. Conclusion: Anterior placement of the intra-articular tibial tunnel aperture was associated with unfavorable radiologic outcomes, including joint space narrowing and progression of the K-L grade. Notably, the normalized anteroposterior distance of the MMPR footprint to the center of the tibial tunnel aperture showed a significant correlation with joint space narrowing, (c) 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.