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Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study
dc.contributor.author | Widera, Pawel | |
dc.contributor.author | Welsing, Paco M.J. | |
dc.contributor.author | Danso, Samuel O. | |
dc.contributor.author | Peelen, Sjaak | |
dc.contributor.author | Kloppenburg, Margreet | |
dc.contributor.author | Loef, Marieke | |
dc.contributor.author | Marijnissen, Anne C. | |
dc.contributor.author | van Helvoort, Eefje M. | |
dc.contributor.author | Blanco García, Francico J | |
dc.contributor.author | Magalhaes, Joana | |
dc.contributor.author | Berenbaum, Francis | |
dc.contributor.author | Haugen, Ida K. | |
dc.contributor.author | Bay-Jensen, Anne-C. | |
dc.contributor.author | Mobasheri, Ali | |
dc.contributor.author | Ladel, Christoph | |
dc.contributor.author | Loughlin, John | |
dc.contributor.author | Lafeber, Floris P.J.G. | |
dc.contributor.author | Lalande, Agnes | |
dc.contributor.author | Larkin, Jonathan | |
dc.contributor.author | Weinans, Harrie | |
dc.contributor.author | Bacardit, Jaume | |
dc.date.accessioned | 2023-09-04T07:39:55Z | |
dc.date.available | 2023-09-04T07:39:55Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Widera P, Welsing PMJ, Danso SO, Peelen S, Kloppenburg M, Loef M, Marijnissen AC, van Helvoort EM, Blanco FJ, Magalhães J, Berenbaum F, Haugen IK, Bay-Jensen AC, Mobasheri A, Ladel C, Loughlin J, Lafeber FPJG, Lalande A, Larkin J, Weinans H, Bacardit J. Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study. Osteoarthr Cartil Open. 2023 Aug 18;5(4):100406. | es_ES |
dc.identifier.issn | 2665-9131 | |
dc.identifier.uri | http://hdl.handle.net/2183/33427 | |
dc.description.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. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ocarto.2023.100406 | es_ES |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC-BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Osteoarthritis | es_ES |
dc.subject | Disease progression prediction | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Patient selection for clinical trials | es_ES |
dc.subject | Inclusion | es_ES |
dc.title | Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Osteoarthritis and Cartilage Open | es_ES |
UDC.volume | 5 | es_ES |
UDC.issue | 4 | es_ES |
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