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Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials

Lookup NU author(s): Dr Paweł Widera, Dr Samuel Danso, Professor John LoughlinORCiD, Professor Jaume Bacardit



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


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.

Publication metadata

Author(s): Widera P, Welsing PMJ, Danso SO, Peelen S, Kloppenburg M, Loef M, Marijnissen AC, vanHelvoort EM, Blanco FJ, Magalhaes J, Berenbaum F, Haugen IK, Bay-Jensen AC, Mobasheri A, Ladel C, Loughlin J, Lafeber FPJG, Lalande A, Larkin J, Weinans H, Bacardit J

Publication type: Article

Publication status: Published

Journal: Osteoarthritis Cartilage Open

Year: 2023

Volume: 5

Issue: 4

Print publication date: 01/12/2023

Online publication date: 18/08/2023

Acceptance date: 13/08/2023

Date deposited: 31/08/2023

ISSN (electronic): 2665-9131

Publisher: Elsevier Ltd


DOI: 10.1016/j.ocarto.2023.100406

PubMed id: 37649530


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