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Lookup NU author(s): Professor John LoughlinORCiD, Dr Paweł Widera, Professor Jaume Bacardit
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within IMI-APPROACH, in relation to the predicted progression scores.Methods: Actual structural progression was measured using minimum Joint Space Width (minJSW). Actual pain (progression) was evaluated using the KOOS pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1, and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. ROC curves were constructed and corresponding AUCs reported. Using Youden's Indices optimal cut-offs were chosen to enable evaluation of both predicted progression scores to identify actual progressors.Results: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively).Conclusion: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors.
Author(s): van Helvoort EM, Jansen MP, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Bay-Jensen AC, Ladel C, Lalande A, Larkin J, Loughlin J, Mobasheri A, Weinans HH, Widera P, Bacardit J, Welsing PMJ, Lafeber FPJG
Publication type: Article
Publication status: Published
Journal: Rheumatology
Year: 2022
Volume: 62
Issue: 1
Pages: 147–157
Print publication date: 01/01/2023
Online publication date: 16/05/2022
Acceptance date: 04/05/2022
Date deposited: 17/05/2022
ISSN (print): 1462-0324
ISSN (electronic): 1462-0332
Publisher: Oxford University Press
URL: https://doi.org/10.1093/rheumatology/keac292
DOI: 10.1093/rheumatology/keac292
Data Access Statement: In order to gain and govern access to the central APPROACH databases, tranSMART and XNAT, access has to be approved by the APPROACH Steering Committee.
PubMed id: 35575381
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