Probabilistic computational modeling of total knee replacement wear

Saikat Pal, Hani Haider, Peter J. Laz, Lucy A. Knight, Paul J. Rullkoetter

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

Polyethylene wear remains a clinically relevant issue affecting total knee replacement (TKR) performance, with considerable variability observed in both clinical retrieval and experimental wear studies. Recently, computational wear simulations have been shown to predict similar results to in vitro and retrieval studies. The objectives of this study were to develop a probabilistic wear prediction model capable of incorporating uncertainty in component alignment, constraint and environmental conditions, to compare computational predictions with experimental results from a knee wear simulator, and to identify the most significant parameters affecting predicted wear performance during simulated gait. The current study utilizes a previously verified wear model; the Archard's law-based wear formulation represents a composite measure, incorporating the effects and relative contributions of kinematics and contact pressure. Predicted wear was in reasonable agreement in trend and magnitude with experimental results. After 5 million cycles, the predicted ranges (1-99%) of variability in linear wear penetration and gravimetric wear were 0.13 mm and 25 mg, respectively, for the input variability levels evaluated. Using correlation-based sensitivity factors, the coefficient of friction, insert tilt and femoral flexion-extension alignment, and the wear coefficient were identified as the parameters most affecting predicted wear. Comparisons of stability, accuracy and efficiency for the Monte Carlo and advanced mean value (AMV) probabilistic methods are also described. The probabilistic wear prediction model provides a time and cost efficient framework to evaluate wear performance, including considerations of malalignment and variability, during the design phase of new implants.

Original languageEnglish (US)
Pages (from-to)701-707
Number of pages7
JournalWear
Volume264
Issue number7-8
DOIs
StatePublished - Mar 15 2008

Fingerprint

Knee prostheses
Wear of materials
retrieval
predictions
alignment
gait
Polyethylene
inserts

Keywords

  • Computational wear simulation
  • Kinematics
  • Knee mechanics
  • Probabilistic
  • TKR

ASJC Scopus subject areas

  • Engineering(all)
  • Mechanical Engineering
  • Surfaces, Coatings and Films

Cite this

Pal, S., Haider, H., Laz, P. J., Knight, L. A., & Rullkoetter, P. J. (2008). Probabilistic computational modeling of total knee replacement wear. Wear, 264(7-8), 701-707. https://doi.org/10.1016/j.wear.2007.06.010

Probabilistic computational modeling of total knee replacement wear. / Pal, Saikat; Haider, Hani; Laz, Peter J.; Knight, Lucy A.; Rullkoetter, Paul J.

In: Wear, Vol. 264, No. 7-8, 15.03.2008, p. 701-707.

Research output: Contribution to journalArticle

Pal, S, Haider, H, Laz, PJ, Knight, LA & Rullkoetter, PJ 2008, 'Probabilistic computational modeling of total knee replacement wear', Wear, vol. 264, no. 7-8, pp. 701-707. https://doi.org/10.1016/j.wear.2007.06.010
Pal, Saikat ; Haider, Hani ; Laz, Peter J. ; Knight, Lucy A. ; Rullkoetter, Paul J. / Probabilistic computational modeling of total knee replacement wear. In: Wear. 2008 ; Vol. 264, No. 7-8. pp. 701-707.
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