Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

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.

Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort.

In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters.

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.

Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores.

Methods: Actual structural progression was measured using minimum joint space width (minJSW).

GaitSmart motion analysis compared to commonly used function outcome measures in the IMI-APPROACH knee osteoarthritis cohort.

Background: There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart® system, which uses inertial measurement units (IMUs), might be a user-friendly and objective method to assess function. This study evaluates the validity and responsiveness of GaitSmart® motion analysis as a function measurement in knee OA and compares this to Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form 36 Health Survey (SF-36), 30s chair stand test, and 40m self-paced walk test.

Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping.

Objectives: Osteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.

Methods: Baseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used.

Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort.

Objectives: To describe the relations between baseline clinical characteristics of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) participants and their predicted probabilities for knee osteoarthritis (OA) structural (S) progression and/or pain (P) progression.

Methods: Baseline clinical characteristics of the IMI-APPROACH participants were used for this study. Radiographs were evaluated according to Kellgren and Lawrence (K&L grade) and Knee Image Digital Analysis.

Relationship between motion, using the GaitSmartTM system, and radiographic knee osteoarthritis: an explorative analysis in the IMI-APPROACH cohort.

Objectives: To assess underlying domains measured by GaitSmartTMparameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and radiographic parameters, and to evaluate if GaitSmart analysis is related to the presence and severity of radiographic knee OA.

Methods: GaitSmart analysis was performed during baseline visits of participants of the APPROACH cohort (n = 297). Principal component analyses (PCA) were performed to explore structure in relationships between GaitSmart parameters alone and in addition to radiographic parameters and PROMs.

Expression of granulocyte macrophage-colony stimulating factor and its receptor in the synovium of osteoarthritis patients is negatively correlated with pain.

Objectives: The crosstalk between the immune and nervous system in the regulation of OA pain is increasingly becoming evident. GM-CSF signals in both systems and might be a new treatment target to control OA pain. Anti GM-CSF treatment has analgesic effects in OA without affecting synovitis scores, suggesting that treatment effects are not caused by local anti-inflammatory effects.

The Expressions of Dickkopf-Related Protein 1 and Frizzled-Related Protein Are Negatively Correlated to Local Inflammation and Osteoarthritis Severity.

Objective: To investigate the presence of WNT antagonists Dickkopf-related protein 1 (DKK1), Frizzled-related protein (FRZB) and BMP antagonist Gremlin 1 (GREM1) in synovial fluid (SF) and serum, respectively, from end-stage knee osteoarthritis (OA) patients, and correlate their expression with other markers of OA.

Design: In a cross-sectional study, SF and serum were collected from OA patients ( = 132). The concentrations of DKK1, FRZB and GREM1 in SF and serum were determined using immunoassays.