University of Oulu

Widera, P., Welsing, P. M. J., Danso, S. O., Peelen, S., Kloppenburg, M., Loef, M., Marijnissen, A. C., Van Helvoort, E. M., Blanco, F. J., Magalhães, J., Berenbaum, F., Haugen, I. K., Bay-Jensen, A.-C., Mobasheri, A., Ladel, C., Loughlin, J., Lafeber, F. P. J. G., Lalande, A., Larkin, J., … Bacardit, J. (2023). Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: The IMI-APPROACH study. Osteoarthritis and Cartilage Open, 5(4), 100406.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials : the IMI-APPROACH study

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Author: Widera, Paweł1; Welsing, Paco M.J.2; Danso, Samuel O.1;
Organizations: 1School of Computing, Newcastle University, Newcastle, UK
2Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
3Lygature, Utrecht, the Netherlands
4Department of Rheumatology, Leiden University Medical Center, Leiden, the Netherlands
5Institute of Biomedical Research, University Hospital of A Coruña, A Coruña, Spain
6APHP Hospital Saint-Antoine, Paris, France
7Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway
8Nordic Bioscience, Herlev, Denmark
9Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
10Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
11Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
12World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Liege, Belgium
13BioBone B.V., Amsterdam, Netherlands
14Bioscience Institute, Newcastle University, International Centre for Life, Newcastle, UK
15Servier International Research Institute, Suresnes, France
16Novel Human Genetics Research Unit, GlaxoSmithKline, Collegeville, United States
17Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
Persistent link:
Language: English
Published: Elsevier, 2023
Publish Date: 2023-09-01


Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.

Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression.

Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57).

Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

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Series: Osteoarthritis and cartilage open
ISSN: 2665-9131
ISSN-E: 2665-9131
ISSN-L: 2665-9131
Volume: 5
Issue: 4
Article number: 100406
DOI: 10.1016/j.ocarto.2023.100406
Type of Publication: A1 Journal article – refereed
Field of Science: 3121 General medicine, internal medicine and other clinical medicine
217 Medical engineering
Funding: The research leading to these results has received support from 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. See and 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.
Copyright information: © 2023 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International (OARSI). This is an open access article under the CC BY-NC-ND license (