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

Authors :
Gielis WP
Weinans H
Welsing PMJ
van Spil WE
Agricola R
Cootes TF
de Jong PA
Lindner C
Source :
Osteoarthritis and cartilage [Osteoarthritis Cartilage] 2020 Jan; Vol. 28 (1), pp. 62-70. Date of Electronic Publication: 2019 Oct 08.
Publication Year :
2020

Abstract

Objective: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis.<br />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.<br />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).<br />Conclusions: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.<br /> (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Details

Language :
English
ISSN :
1522-9653
Volume :
28
Issue :
1
Database :
MEDLINE
Journal :
Osteoarthritis and cartilage
Publication Type :
Academic Journal
Accession number :
31604136
Full Text :
https://doi.org/10.1016/j.joca.2019.09.005