[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121895-en":3,"doc-seo-121895-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121895,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials - the IMI-APPROACH study","Osteoarthritis clinical trials require progression-enriched patient populations to efficiently evaluate disease-modifying effects of new treatments. The IMI-APPROACH study developed and validated a two-stage, machine learning-supported recruitment strategy that ranked candidates by likelihood of progression. First-stage models used pre-existing cohort data for screening-visit selection, and the second stage used screening data to support final inclusion. Effectiveness was assessed by actual 24-month progression and model performance.","| Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study |  |  |  |\n| --- | --- | --- | --- |\n| Paweł Widera a, Paco M.J. Welsingb, Samuel O. Danso a, Sjaak Peelen c, Margreet Kloppenburg d, Marieke Loefd, Anne C. Marijnissen b, Eefje M. van Helvoortb, Francisco J. Blanco e,\u003Cbr>Joana Magalh~aes e, Francis Berenbaum f, Ida K. Haugen g, Anne-Christine Bay-Jensen h, Ali Mobasherib, i,j, k, l, Christoph Ladel m, John Loughlin n, Floris P.J.G. Lafeber b, Agns Lalande o,\u003Cbr>*\u003Cbr>Jonathan Larkin p, Harrie Weinans q, Jaume Bacardit a,\u003Cbr>a School of Computing, Newcastle University, Newcastle, UK\u003Cbr>b Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands c Lygature, Utrecht, the Netherlands\u003Cbr>d Department of Rheumatology, Leiden University Medical Center, Leiden, the Netherlands e Institute of Biomedical Research, University Hospital of A Coru~na, A Coru~na, Spain fAPHP Hospital Saint-Antoine, Paris, France\u003Cbr>g Division of Rheumatology and Research, Diakonhjemmet Hospital, Oslo, Norway h Nordic Bioscience, Herlev, Denmark\u003Cbr>i Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland j Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania k Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China\u003Cbr>l World Health Organization Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Liege, Belgium m BioBone B. V., Amsterdam, Netherlands\u003Cbr>n Bioscience Institute, Newcastle University, International Centre for Life, Newcastle, UK\u003Cbr>o Servier International Research Institute, Suresnes, France\u003Cbr>p Novel Human Genetics Research Unit, GlaxoSmithKline, Collegeville, United States\u003Cbr>q Department of Orthopedics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling Editor: Professor H Madry |  | 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 inpatient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.\u003Cbr>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.\u003Cbr>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 þ 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 painrelated 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 þ S (median rank 65 vs 143, AUC ¼ 0.75), P (median rank 77 vs 143, AUC ¼ 0.71), and S patients (median rank 107 vs 143, AUC ¼ 0.57).\u003Cbr>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. |  |\n| Keywords:\u003Cbr>Osteoarthritis\u003Cbr>Disease progression p","cbCaib3B8EP9aLQT","https://ap.wps.com/l/cbCaib3B8EP9aLQT","pdf",2071494,1,9,"English","en",105,"# Abstract\n## Objectives\n## Design\n## Results\n## Conclusions\n# Introduction\n## Patient selection challenges in clinical trials\n## Rationale for enrichment in osteoarthritis","[{\"question\":\"What problem does the IMI-APPROACH study address in osteoarthritis clinical trials?\",\"answer\":\"Clinical trials need progression-enriched patient populations so disease-modifying effects can be observed within the trial duration. The study targets the bottleneck of selecting the right patients for expected progression.\"},{\"question\":\"How does the machine learning recruitment strategy work?\",\"answer\":\"It uses a two-stage process. Stage one ranks and selects candidates using models trained on pre-existing cohort data, and stage two refines final inclusion using screening-visit data.\"},{\"question\":\"What outcomes did the study observe after applying the strategy?\",\"answer\":\"Among screened candidates, progression related to pain and/or structure was measured over 24 months, with non-progressors reduced versus an unenriched population. Model discrimination performance was reported using AUC values for pain- and structure-related progression.\"}]","Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials - the IMI-APPROACH study | PDF",1785807626,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-and-validation-of-a-machine-learning-supported-strategy-of-patient-selection-for-osteoarthritis-clinical-trials-the-imi-approach-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-and-validation-of-a-machine-learning-supported-strategy-of-patient-selection-for-osteoarthritis-clinical-trials-the-imi-approach-study/121895/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the IMI-APPROACH study address in osteoarthritis clinical trials?","Question",{"text":75,"@type":76},"Clinical trials need progression-enriched patient populations so disease-modifying effects can be observed within the trial duration. The study targets the bottleneck of selecting the right patients for expected progression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning recruitment strategy work?",{"text":80,"@type":76},"It uses a two-stage process. Stage one ranks and selects candidates using models trained on pre-existing cohort data, and stage two refines final inclusion using screening-visit data.",{"name":82,"@type":73,"acceptedAnswer":83},"What outcomes did the study observe after applying the strategy?",{"text":84,"@type":76},"Among screened candidates, progression related to pain and/or structure was measured over 24 months, with non-progressors reduced versus an unenriched population. 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