Simulation of preoperative flexion contracture in a computational model of total knee arthroplasty: Development and evaluation. Academic Article uri icon

Overview

abstract

  • Preoperative flexion contracture is a risk factor for patient dissatisfaction following primary total knee arthroplasty (TKA). Previous studies utilizing surgical navigation technology and cadaveric models attempted to identify operative techniques to correct knees with flexion contracture and minimize undesirable outcomes such as knee instability. However, no consensus has emerged on a surgical strategy to treat this clinical condition. Therefore, the purpose of this study was to develop and evaluate a computational model of TKA with flexion contracture that can be used to devise surgical strategies that restore knee extension and to understand factors that cause negative outcomes. We developed six computational models of knees implanted with a posteriorly stabilized TKA using a measured resection technique. We incorporated tensions in the collateral ligaments representative of those achieved in TKA using reference data from a cadaveric experiment and determined tensions in the posterior capsule elements in knees with flexion contracture by simulating a passive extension exam. Subject-specific extension moments were calculated and used to evaluate the amount of knee extension that would be restored after incrementally resecting the distal femur. Model predictions of the extension angle after resecting the distal femur by 2 and 4 mm were within 1.2° (p ≥ 0.32) and 1.6° (p ≥ 0.25), respectively, of previous studies. Accordingly, the presented computational method could be a credible surrogate to study the mechanical impact of flexion contracture in TKA and to evaluate its surgical treatment.

publication date

  • March 9, 2021

Research

keywords

  • Arthroplasty, Replacement, Knee
  • Contracture

Identity

PubMed Central ID

  • PMC8183383

Scopus Document Identifier

  • 85104291984

Digital Object Identifier (DOI)

  • 10.1016/j.jbiomech.2021.110367

PubMed ID

  • 33887615

Additional Document Info

volume

  • 120