Long, MJ;
Papi, E;
Duffell, LD;
McGregor, AH;
(2017)
Predicting knee osteoarthritis risk in injured populations.
Clinical Biomechanics
, 47
, Article Volume 47, August 2017, Pages 87-95. 10.1016/j.clinbiomech.2017.06.001.
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Abstract
BACKGROUND: Individuals who suffered a lower limb injury have an increased risk of developing knee osteoarthritis. Early diagnosis of osteoarthritis and the ability to track its progression is challenging. This study aimed to explore links between self-reported knee osteoarthritis outcome scores and biomechanical gait parameters, whether self-reported outcome scores could predict gait abnormalities characteristic of knee osteoarthritis in injured populations and, whether scores and biomechanical outcomes were related to osteoarthritis severity via Spearman's correlation coefficient. METHODS: A cross-sectional study was conducted with asymptomatic participants, participants with lower-limb injury and those with medial knee osteoarthritis. Spearman rank determined relationships between knee injury and outcome scores and hip and knee kinetic/kinematic gait parameters. K-Nearest Neighbour algorithm was used to determine which of the evaluated parameters created the strongest classifier model. FINDINGS: Differences in outcome scores were evident between groups, with knee quality of life correlated to first and second peak external knee adduction moment (0.47, 0.55). Combining hip and knee kinetics with quality of life outcome produced the strongest classifier (1.00) with the least prediction error (0.02), enabling classification of injured subjects gait as characteristic of either asymptomatic or knee osteoarthritis subjects. When correlating outcome scores and biomechanical outcomes with osteoarthritis severity only maximum external hip and knee abduction moment (0.62, 0.62) in addition to first peak hip adduction moment (0.47) displayed significant correlations. INTERPRETATION: The use of predictive models could enable clinicians to identify individuals at risk of knee osteoarthritis and be a cost-effective method for osteoarthritis screening.
| Type: | Article |
|---|---|
| Title: | Predicting knee osteoarthritis risk in injured populations |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.clinbiomech.2017.06.001 |
| Publisher version: | https://doi.org/10.1016/j.clinbiomech.2017.06.001 |
| Language: | English |
| Additional information: | © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). |
| Keywords: | Osteoarthritis, Knee Injury, Gait, K-Nearest Neighbour |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1566676 |
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