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Objective: As part of the first phase of the OARSI Early-stage Symptomatic Knee Osteoarthritis (EsSKOA) initiative, we explored the first symptoms and experiences recalled by individuals with knee osteoarthritis (OA). Design: This qualitative study, informed by qualitative description, was a secondary analysis of focus groups (n ​= ​17 groups) and one-on-one interviews (n ​= ​3) conducted in 91 individuals living with knee OA as part of an international study to better understand the OA pain experience. In each focus group or interview, participants were asked to describe their first symptoms of knee OA. We inductively coded these transcripts and conducted thematic analysis. Results: Mean age of participants was 70 years (range 47–92) and 68 ​% were female. We developed four overarching themes: Insidious and Episodic Onset, Diverse Early Symptoms, Must be Something Else, and Adjustments. Participants described the gradual and intermittent way in which symptoms of knee OA developed over many years; many could not identify a specific starting point. Participants described diverse initial knee symptoms, including activity-exacerbated joint pain, stiffness and crepitus. Most participants dismissed early symptoms or rationalized their presence, employing various strategies to enable continued participation in recreational and daily activities. Few sought medical attention until physical functioning was demonstrably impacted. Conclusions: The earliest symptoms of knee OA are frequently insidious in onset, episodic and present long before individuals present to health professionals. These results highlight challenges to identifying people with knee OA early and support the development of specific classification criteria for EsSKOA to capture individuals at an early stage.

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.

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. Methods: Data quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression. Results: Three dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain. Conclusions: This work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.

Hand osteoarthritis is the most common joint condition and is associated with significant morbidity. It is of paramount importance that patients are thoroughly assessed and examined when complaining of hand stiffness, pain, deformity or disability and that the patient’s concerns and expectations are addressed by the healthcare professional. In 2019 the American College of Rheumatology and Arthritis Foundation (ACR/AF) produced guidelines which included recommendations for the treatment of hand osteoarthritis. An ESCEO expert working group (including patients) was convened and composed this paper with the aim to assess whether these guidelines were appropriate for the treatment of hand osteoarthritis therapy in Europe and whether they met with the ESCEO patient-centered approach. Indeed, patients are the key stakeholders in healthcare and eliciting the patient’s preference is vital in the context of an individual consultation but also for informing research and policy-making. The patients involved in this working group emphasised the often-neglected area of aesthetic changes in hand osteoarthritis, importance of developing pharmacological therapies which can alleviate pain and disability and the need of the freedom to choose which approach (out of pharmacological, surgical or non-pharmacological) they wished to pursue. Following robust appraisal, it was recommended that the ACR/AF guidelines were suitable for a European context (as described within the body of the manuscript) and it was emphasised that patient preferences are key to the success of individual consultations, future research and future policy-making.

Knee osteoarthritis (OA) is one of the most common and disabling medical conditions. In the case of moderate to severe pain, a single intervention may not be sufficient to allay symptoms and improve quality of life. Examples include first-line, background therapy with symptomatic slow-acting drugs for OA (SYSADOAs) or non-steroidal anti-inflammatory drugs (NSAIDs). Therefore, the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) performed a review of a multimodal/multicomponent approach for knee OA therapy. This strategy is a particularly appropriate solution for the management of patients affected by knee OA, including those with pain and dysfunction reaching various thresholds at the different joints. The multimodal/multicomponent approach should be based, firstly, on different combinations of non-pharmacological and pharmacological interventions. Potential pharmacological combinations include SYSADOAs and NSAIDs, NSAIDs and weak opioids, and intra-articular treatments with SYSADOAs/NSAIDs. Based on the available evidence, most combined treatments provide benefit beyond single agents for the improvement of pain and other symptoms typical of knee OA, although further high-quality studies are required. In this work, we have therefore provided new, patient-centered perspectives for the management of knee OA, based on the concept that a multimodal, multicomponent, multidisciplinary approach, applied not only to non-pharmacological treatments but also to a combination of the currently available pharmacological options, will better meet the needs and expectations of patients with knee OA, who may present with various phenotypes and trajectories.

To compare reliabilities of assessing synovitis in hand osteoarthritis (OA) using Magnetic Resonance Imaging (MRI) with/without gadolinium (Gd). Three readers scored synovitis on non-enhanced two-dimensional (2D) proton density (PD)-weighted MRI and Gd-enhanced (3D) MRI of hand joints in 20 patients. Inter-reader reliabilities were examined. Reliability was good for Gd-enhanced MRI, but poor for non-enhanced PD-weighted MRI (intraclass correlation coefficient 0.83 and 0.21, respectively). Agreement between the two sequences was poor (weighted kappa 0.18). Gd-enhanced MRI was more reliable than PD-weighted MRI for assessing synovitis. Gd-enhancement, but also resolution and tissue contrast, might have contributed to this.

Knee osteoarthritis (OA) is a heterogeneous disease associated with substantial effects on quality of life, and its clinical management is difficult. Among the several available guidelines for the management of knee OA, those from OARSI and ESCEO were updated in 2019. Here, we examine the similarities and differences between these two guidelines and provide a narrative to help guide health-care providers through the complexities of non-surgical management of knee OA. OARSI and ESCEO both recommend education, structured exercise and weight loss as core treatments, topical NSAIDs as first-line treatments and oral NSAIDs and intra-articular injections for persistent pain. Low-dose, short-term acetaminophen, pharmaceutical grade glucosamine and chondroitin sulfate are recommended by ESCEO whereas OARSI strongly recommends against their use (including all glucosamine and chondroitin formulations). Despite this difference, the two guidelines are consistent in the majority of their recommendations and provide useful treatment recommendations for individuals with OA and health-care providers.

Objectives: To compare reliabilities of assessing synovitis in hand osteoarthritis (OA) using Magnetic Resonance Imaging (MRI) with/without gadolinium (Gd). Methods: Three readers scored synovitis on non-enhanced two-dimensional (2D) proton density (PD)-weighted MRI and Gd-enhanced (3D) MRI of hand joints in 20 patients. Inter-reader reliabilities were examined. Results: Reliability was good for Gd-enhanced MRI, but poor for non-enhanced PD-weighted MRI (intraclass correlation coefficient 0.83 and 0.21, respectively). Agreement between the two sequences was poor (weighted kappa 0.18). Conclusion: Gd-enhanced MRI was more reliable than PD-weighted MRI for assessing synovitis. Gd-enhancement, but also resolution and tissue contrast, might have contributed to this.