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@article{https://doi.org/10.1002/jor.25363,

title = {Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis},

author = {Kathryn L. Young-Shand and Patrice C. Roy and Michael J. Dunbar and Syed S. R. Abidi and Janie L. Astephen Wilson},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jor.25363},

doi = {https://doi.org/10.1002/jor.25363},

year  = {2022},

date = {2022-05-10},

urldate = {2022-05-10},

journal = {Journal of Orthopaedic Research},

abstract = {Abstract Knee osteoarthritis patient phenotyping is relevant to developing targeted treatments and assessing the treatment efficacy of total knee arthroplasty (TKA). This study aimed to identify clusters among TKA candidates based on demographic and knee mechanic features during gait, and compare gait changes between clusters postoperatively. TKA patients underwent 3D gait analysis 1-week pre (n = 134) and 1-year post-TKA (n = 105). Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms, extracting major patterns of variability. Age, sex, body mass index, gait speed, and frontal and sagittal pre-TKA angle and moment PC scores previously identified as relevant to TKA outcomes were standardized (mean = 0},

keywords = {gait analysis, knee biomechanics, Machine Learning, phenotypes, Total knee arthroplasty},

pubstate = {published},

tppubtype = {article}

}