Effect of ACL graft material on joint forces during a simulated in vivo motion in the porcine knee: Examining force during the initial cycles

Abstract

This study compared three‐dimensional forces in knees containing anterior cruciate ligament (ACL) graft materials versus the native porcine ACL. A six‐degree‐of‐freedom (DOF) robot simulated gait while recording the joint forces and moments. Knees were subjected to 10 cycles of simulated gait in intact, ACL‐deficient, and ACL‐reconstructed knee states to examine time zero biomechanical performance. Reconstruction was performed using bone‐patellar tendon‐bone allograft (BPTB), reconstructive porcine tissue matrix (RTM), and an RTM‐polymer hybrid (Hybrid). Forces and moments were examined about anatomic DOFs throughout the gait cycle and at three key points during gait: heel strike (HS), mid stance (MS), toe off (TO). Compared to native ACL, each graft restored antero‐posterior (A‐P) forces throughout gait. However, all failed to mimic normal joint forces in other DOFs. For example, each reconstructed knee showed greater compressive forces at HS and TO compared to the native ACL knee. Overall, the Hybrid graft restored more of the native ACL forces following reconstruction than did BPTB, while RTM grafts were the least successful. If early onset osteoarthritis is in part caused by altered knee kinematics, then understanding how reconstruction materials restore critical force generation during gait is an essential step in improving a patient's long‐term prognosis. © 2014 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 32:1458–1463, 2014.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 06, 2014
Source ID
10.1002/jor.22704

Entities

People

  • Christopher T. Wagner
  • Daniel V. Boguszewski
  • David L. Butler
  • Jason T. Shearn

Organizations

  • The College of New Jersey
  • United States Department of Defense
  • University of California
  • University of Cincinnati

Tags

Fields of Study

  • Medicine

Readers

  • Neurotrauma and Rehabilitation Medicine.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy