Predicting knee adduction moment response to gait retraining with minimal clinical data

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression an...

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Bibliographic Details
Published in:PLoS computational biology 2022-05, Vol.18 (5), p.e1009500-e1009500
Main Authors: Rokhmanova, Nataliya, Kuchenbecker, Katherine J, Shull, Peter B, Ferber, Reed, Halilaj, Eni
Format: Article
Language:English
Subjects:
Arthritis
Biology and Life Sciences
Biomedical materials
Body weight
Clinical trials
Complications and side effects
Data collection
Datasets
Feet
Fingers & toes
Gait
Health aspects
Injuries
Instrumentation
Instruments
Intervention
Knee
Laboratories
Medicine and Health Sciences
Model testing
Osteoarthritis
Pain
Patients
Physical Sciences
Prediction models
Regression models
Research and Analysis Methods
Scholarships & fellowships
Social Sciences
Training
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Summary:Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data-a set of six features easily obtained in the clinic-to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009500