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Predictive models represent a cheaper alternative for
osteoarthritis screening; clinicians can easily estimate the individuals at
greater risk of knee OA.
Osteoarthritis is one of the most prevalent diseases throughout the world. The person who suffered from the injuries such as of lower limb has higher chances of developing knee osteoarthritis. However, its early diagnosis and tracking its development is very difficult. To solve this issue, scientist conducted a cross-sectional study.
The objective of this study was to investigate self-reported knee
osteoarthritis outcome scores and biomechanical gait parameters association and
whether these outcome scores predict gait abnormalities characteristic of knee
osteoarthritis in injured populations. Scientists also evaluated whether
outcome scores and biomechanical outcomes linked to osteoarthritis severity via
Spearman's correlation coefficient or not.
The study operated among asymptomatic patients with lower-limb injury and
medial knee osteoarthritis. The evaluation involved a Spearman rank which
helped to evaluate the association between outcome scores, knee injury and hip
& knee kinetic/kinematic gait parameters. K-Nearest Neighbour algorithm was
also conducted to evaluate which of the evaluated parameters that designed the
strongest classifier model.
The outcome score differences were
visible among groups, with the knee quality of life associated with first &
second peak external knee adduction moment. Further, by linking hip and knee
kinetics with the quality of life outcome, the most robust classifier formed
with the least prediction error. This enabled the classification of injured
subjects gait as characteristic of either asymptomatic or knee osteoarthritis
subjects. Moreover, when the biomechanical outcomes and correlating outcome
scores with osteoarthritis severity, only maximum external hip and knee
adduction moment with first peak hip
adduction moment shown prominent associations.
Clin Biomech (Bristol, Avon). 2017 Jun 12;47:87-95
Predicting knee osteoarthritis risk in injured populations
Long MJ et al.
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