Eefje M van Helvoort, Mylène P Jansen, Anne C A Marijnissen, Margreet Kloppenburg, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Anne-Christine C Bay-Jensen, Christoph Ladel, Agnes Lalande, Jonathan Larkin, John Loughlin, Ali Mobasheri, Harrie H Weinans, Pawel Widera, Jaume Bacardit, Paco M J Welsing, Floris P J G Lafeber, Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort, Rheumatology, Volume 62, Issue 1, January 2023, Pages 147–157, https://doi.org/10.1093/rheumatology/keac292
Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort
|Author:||van Helvoort, Eefje M.1; Jansen, Mylène P.1; Marijnissen, Anne C. A.1;|
1Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht
2Department of Rheumatology
3Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
4Grupo de Investigación de Reumatologia (GIR), INIBIC-Complejo Hospitalario Universitario de A Coruña, SERGAS, Centro de Investigación CICA, Departamento de Fisiotherapia y Medicina, Universidad de A Coruña, A Coruña, Spain
5Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway
6Department of Rheumatology, AP-HP Saint-Antoine Hospital
7INSERM, Centre de Recherche Saint-Antoine, Sorbonne University, Paris, France
8Nordic Bioscience A/S, Herlev, Denmark
9BioBone B.V., Amsterdam, The Netherlands
10Institut de Recherches Internationales Servier, Suresnes, France
11GlaxoSmithKline USA, Collegeville, PA, USA
12Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
13Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulo, Oulo, Finland
14Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
15Department of Joint Surgery, First Affiliated Hospital of Sun Yat- sen University, Guangzhou, China
16World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and ging, Liege, Belgium
17Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
18School of Computing, Newcastle University, Newcastle upon Tyne, UK
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231108143539
Oxford University Press,
|Publish Date:|| 2023-11-08
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 the 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 Knee injury and Osteoarthritis Outcomes Score (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. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden’s index, 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.
Trial registration: ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.
|Pages:||147 - 157|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3121 General medicine, internal medicine and other clinical medicine
This work was supported by the Innovative Medicines Initiative Joint Undertaking under Grant Agreement no. 115770, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. See www.imi.europa.eu and www.approachproject.eu.
© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org.