Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials : the IMI-APPROACH study
Widera, Paweł; Welsing, Paco M.J.; Danso, Samuel O.; Peelen, Sjaak; Kloppenburg, Margreet; Loef, Marieke; Marijnissen, Anne C.; van Helvoort, Eefje M.; Blanco, Francisco J.; Magalhães, Joana; 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; Bacardit, Jaume (2023-08-18)
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. https://doi.org/10.1016/j.ocarto.2023.100406
© 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe20230901115468
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [38358]
