Widera, P, Welsing, Paco M.J., Danso, S. O, Peelen, Sjaak, Kloppenburg, Margreet, Loef, Marieke, Marijnissen, Anne C., van Helvoort, Eefje M., Blanco, Franciso J., Magalhães, Joanna, Berenbaum, Francis, Haugen, Ida K., Bay-Jensen, Anne-Christine, Mobasheri, Ali, Ladel, Christoph, Loughlin, John, Lafeber, Floris P.J.G., Lalande, Agnès, Larkin, Jonathan, Weinans, Harrie and Jaume, Baccardit (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). p. 100406. ISSN 2665-9131
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Abstract
Abstract
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.
| Item Type: | Article |
|---|---|
| Depositing User: | Sam Danso |
| URI: | https://sure.sunderland.ac.uk/id/eprint/17014 |
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