Predicting knee osteoarthritis risk in injured populations
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 biomechanic...
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| Veröffentlicht in: | Clinical biomechanics (Bristol) Jg. 47; H. NA; S. 87 - 95 |
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England
Elsevier Ltd
01.08.2017
Elsevier Science |
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| ISSN: | 0268-0033, 1879-1271, 1879-1271 |
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| Abstract | 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.
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.
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.
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.
•Individuals with lower limb injury have increased a risk of early on-set osteoarthritis.•Current diagnosis techniques only identify established osteoarthritis.•Biomechanical and self-reported questionnaires may be strongly correlated in gait.•Prediction models could aid clinicians identify risk of osteoarthritis during gait.•Self-reported questionnaires have low correlation to radiographic knee scans. |
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| AbstractList | •
Individuals with lower limb injury have increased a risk of early on-set osteoarthritis.
•
Current diagnosis techniques only identify established osteoarthritis.
•
Biomechanical and self-reported questionnaires may be strongly correlated in gait.
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Prediction models could aid clinicians identify risk of osteoarthritis during gait.
•
Self-reported questionnaires have low correlation to radiographic knee scans. 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.BACKGROUNDIndividuals 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.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.METHODSA 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.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.FINDINGSDifferences 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.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.INTERPRETATIONThe use of predictive models could enable clinicians to identify individuals at risk of knee osteoarthritis and be a cost-effective method for osteoarthritis screening. 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. 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. 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. 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. •Individuals with lower limb injury have increased a risk of early on-set osteoarthritis.•Current diagnosis techniques only identify established osteoarthritis.•Biomechanical and self-reported questionnaires may be strongly correlated in gait.•Prediction models could aid clinicians identify risk of osteoarthritis during gait.•Self-reported questionnaires have low correlation to radiographic knee scans. 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. 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. 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. 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. 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. |
| Author | Long, Michael J. McGregor, Alison H. Papi, Enrica Duffell, Lynsey D. |
| AuthorAffiliation | a Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK b Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK |
| AuthorAffiliation_xml | – name: a Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK – name: b Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK |
| Author_xml | – sequence: 1 givenname: Michael J. surname: Long fullname: Long, Michael J. organization: Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK – sequence: 2 givenname: Enrica orcidid: 0000-0003-0282-7223 surname: Papi fullname: Papi, Enrica email: e.papi@imperial.ac.uk organization: Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK – sequence: 3 givenname: Lynsey D. surname: Duffell fullname: Duffell, Lynsey D. organization: Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK – sequence: 4 givenname: Alison H. surname: McGregor fullname: McGregor, Alison H. organization: Department of Surgery and Cancer, Imperial College London, Room 7L16, Floor 7, Laboratory Block, Charing Cross Hospital, London W6 8RF, UK |
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| Keywords | Knee Gait K-Nearest Neighbour Osteoarthritis Injury |
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| Snippet | Individuals who suffered a lower limb injury have an increased risk of developing knee osteoarthritis. Early diagnosis of osteoarthritis and the ability to... Abstract Background Individuals who suffered a lower limb injury have an increased risk of developing knee osteoarthritis. Early diagnosis of osteoarthritis... • Individuals with lower limb injury have increased a risk of early on-set osteoarthritis. • Current diagnosis techniques only identify established... |
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| SubjectTerms | Adult Biomechanical Phenomena Cross-Sectional Studies Female Gait Gait - physiology Humans Injury K-Nearest Neighbour Knee Knee Injuries - diagnosis Knee Injuries - physiopathology Knee Joint Male Middle Aged Osteoarthritis Osteoarthritis, Knee - diagnosis Osteoarthritis, Knee - physiopathology Physical Medicine and Rehabilitation Quality of Life Surveys and Questionnaires |
| Title | Predicting knee osteoarthritis risk in injured populations |
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