An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study

An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study

Gielis, W. P. (University Medical Center Utrecht)
Weinans, Harrie (TU Delft Biomaterials & Tissue Biomechanics; University Medical Center Utrecht)
Welsing, P. M.J. (University Medical Center Utrecht)
van Spil, W. E. (University Medical Center Utrecht)
Agricola, R. (Erasmus MC)
Cootes, T. F. (The University of Manchester)
de Jong, P. A. (University Medical Center Utrecht)
Lindner, C. (The University of Manchester)

2020

Objective: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis. Design: We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (Kellgren–Lawrence grade ≥2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included. Results: 84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74–7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757–0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833–0.895). Conclusions: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.

Epidemiology
Hip osteoarthritis
Imaging
Statistical shape analysis

http://resolver.tudelft.nl/uuid:453cd768-2eeb-4ca0-81e3-a3e16dec33cc

https://doi.org/10.1016/j.joca.2019.09.005

1063-4584

Osteoarthritis and Cartilage, 28 (1), 62-70

Institutional Repository

journal article

© 2020 W. P. Gielis, Harrie Weinans, P. M.J. Welsing, W. E. van Spil, R. Agricola, T. F. Cootes, P. A. de Jong, C. Lindner