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.

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).

Osteoarthritis endotype discovery via clustering of biochemical marker data.

Objectives: Osteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.

Method: Data quality assessment was performed to design appropriate data preprocessing techniques.

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.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded.