Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

Autor: Kaneda JM; Department of Bioengineering, Stanford University, Stanford, CA, United States. Electronic address: jkaneda@stanford.edu., Seagers KA; Department of Mechanical Engineering, Stanford University, Stanford, CA, United States., Uhlrich SD; Department of Bioengineering, Stanford University, Stanford, CA, United States., Kolesar JA; Department of Bioengineering, Stanford University, Stanford, CA, United States; Musculoskeletal Research Lab, VA Palo Alto Healthcare System, Palo Alto, CA, United States., Thomas KA; Department of Biomedical Data Science, Stanford University, Stanford, CA, United States., Delp SL; Department of Bioengineering, Stanford University, Stanford, CA, United States; Department of Mechanical Engineering, Stanford University, Stanford, CA, United States; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, United States.
Jazyk: angličtina
Zdroj: Journal of biomechanics [J Biomech] 2023 May; Vol. 152, pp. 111569. Date of Electronic Publication: 2023 Mar 25.
DOI: 10.1016/j.jbiomech.2023.111569
Abstrakt: Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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