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

[Abstract] 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. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Numeric Rating Scale (NRS) were used to evaluate pain. Predicted progression scores for each individual were determined using machine learning models. Pearson correlation coefficients were used to evaluate correlations between scores for predicted progression and baseline characteristics. T-tests and χ2 tests were used to evaluate differences between participants with high versus low progression scores. Results: Participants with high S progressions score were found to have statistically significantly less structural damage compared with participants with low S progression scores (minimum Joint Space Width, minJSW 3.56 mm vs 1.63 mm; p<0.001, K&L grade; p=0.028). Participants with high P progression scores had statistically significantly more pain compared with participants with low P progression scores (KOOS pain 51.71 vs 82.11; p<0.001, NRS pain 6.7 vs 2.4; p<0.001). Conclusions: The baseline minJSW of the IMI-APPROACH participants contradicts the idea that the (predicted) course of knee OA follows a pattern of inertia, where patients who have progressed previously are more likely to display further progression. In contrast, for pain progressors the pattern of inertia seems valid, since participants with high P score already have more pain at baseline compared with participants with a low P score

Neuropathic Pain in the IMI-APPROACH Knee Osteoarthritis Cohort: Prevalence and Phenotyping

The study is registered under clinicaltrials.gov nr: NCT03883568.[Abstract] 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. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component. Results: OA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different. Conclusions: OA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments

The OMERACT-OARSI Core Domain Set for Measurement in Clinical Trials of Hip and/or Knee Osteoarthritis

Objective: To update the 1997 OMERACT-OARSI (Outcome Measures in Rheumatology-Osteoarthritis Research Society International) core domain set for clinical trials in hip and/or knee osteoarthritis (OA). Methods: An initial review of the COMET database of core outcome sets (COS) was undertaken to identify all domains reported in previous COS including individuals with hip and/or knee OA. These were presented during 5 patient and health professionals/researcher meetings in 3 continents (Europe, Australasia, North America). A 3-round international Delphi survey was then undertaken among patients, healthcare professionals, researchers, and industry representatives to gain consensus on key domains to be included in a core domain set for hip and/or knee OA. Findings were presented and discussed in small groups at OMERACT 2018, where consensus was obtained in the final plenary. Results: Four previous COS were identified. Using these, and the patient and health professionals/researcher meetings, 50 potential domains formed the Delphi survey. There were 426 individuals from 25 different countries who contributed to the Delphi exercise. OMERACT 2018 delegates (n = 129) voted on candidate domains. Six domains gained agreement as mandatory to be measured and reported in all hip and/or knee OA clinical trials: pain, physical function, quality of life, and patient’s global assessment of the target joint, in addition to the mandated core domain of adverse events including mortality. Joint structure was agreed as mandatory in specific circumstances, i.e., depending on the intervention. Conclusion: The updated core domain set for hip and/or knee OA has been agreed upon. Work will commence to determine which outcome measurement instrument should be recommended to cover each core domain

A first-in-human, double-blind, randomised, placebo-controlled, dose ascending study of intra-articular rhFGF18 (sprifermin) in patients with advanced knee osteoarthritis

OBJECTIVES: To evaluate the safety of intra-articular sprifermin (primary), and to evaluate systemic exposure, biomarkers, histology, and other cartilage parameters in patients with advanced osteoarthritis (OA).METHODS: This was a first-in-human, double-blind, randomised, placebo-controlled trial of single and multiple ascending doses of sprifermin from 3-300 μg in knee OA patients scheduled for total knee replacement. Patients were randomised 3:1 to sprifermin or placebo, injected into the target knee once or once weekly for 3 weeks, and followed-up for 24 weeks.RESULTS: Fifty-five patients were treated with sprifermin, 25 with single and 30 with multiple doses, 18 received placebo. There was no clear difference between the active and placebo groups in incidence, severity, and nature of reported treatment emergent adverse events. Acute inflammatory reactions were slightly more common with sprifermin 300 μg, but none led to discontinuation. No clear difference was seen between placebo and sprifermin in physician-assessed local tolerability, pain, or swelling in the knee. No meaningful changes over time, or differences between treatment groups, were observed for safety laboratory parameters or ECG. Although individual abnormalities were observed, no patterns were evident suggesting a relation to treatment or potential safety concern. No systemic sprifermin exposure, anti-FGF18 antibodies, or clear-cut effects on systemic biomarkers were detected.CONCLUSIONS: This first clinical trial of sprifermin revealed no serious safety concerns, although larger studies are needed. The possibility of positive effects of intra-articular sprifermin on histological and other cartilage parameters in knee OA also warrant further investigation

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

Abstract 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. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20–25% the number of patients who show no progression. This result might lead to more efficient clinical trials

Test–retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort

[Abstract] Objective. To investigate the test–retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort – an exploratory, 5-center, 2-year prospective follow-up cohort. Design. Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 ± 7.1 years, body mass index (BMI): 28.1 ± 5.3 kg/m2, 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test–retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test–retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: −211 μm) for the quartile with the highest vs the quartile with the lowest s-scores. Results. The test–retest precision was 69 μm for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached −174 μm (95% CI: [−207, −141] μm, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]). Conclusion. IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score