Predictors of Placebo Response to Local (Intra-Articular) Therapy In Osteoarthritis:An Individual Participant Data Meta-Analysis

Objective: We undertook this study to evaluate potential predictors of placebo response with intra-articular (IA) injections for knee/hip osteoarthritis (OA) using individual participant data (IPD) from existing trials.Methods: Randomized placebo-controlled trials evaluating IA glucocorticoid or hyaluronic acid published to September 2018 were selected. IPD for disease characteristics and outcome measures were acquired. Potential predictors of placebo response included participant characteristics, pain severity, intervention, and trial design. Placebo response was defined as at least a 20% reduction in baseline pain. Logistic regression models and odds ratios were computed as effect measures to evaluate patient and pain mechanisms and then pooled using a random effects model. Generalized mixed-effect models were applied to intervention and trial characteristics. Results: Of 56 eligible trials, 6 shared data, and these were combined with the existing 4 OA Trial Bank studies, yielding 10 studies with IPD of 621 placebo participants for analysis. In the total placebo population, at short-term follow-up, the use of local anesthetic and ultrasound guidance were associated with reduced odds of placebo response. At midterm follow-up, mid- to long-term trial duration was associated with increased odds of placebo response, and worse baseline function scores were associated with reduced odds of a placebo response. Conclusion: The administration of local anesthetics or ultrasound guidance may reduce IA placebo response at short-term follow-up. At midterm follow-up, participants with worse baseline function scores may be less likely to respond to IA placebo, and mid- to long-term trial duration may enhance the placebo response. Further studies are required to corroborate these potential predictors of IA placebo response.</p

Predictors of placebo response to local (intra-articular) therapy in osteoarthritis: an individual patient data meta-analysis protocol

Abstract Introduction Osteoarthritis (OA) is a highly prevalent and disabling condition with limited safe and effective treatment options. Intra-articular therapies are increasingly being used, however whether the effect of these agents is due to active treatment or placebo remains unclear. As the placebo response can be attributed to multiple factors, assessment of the placebo response using individual patient data (IPD) meta-analysis will give insight into the different modifiers of response to placebo. The aim of this IPD meta-analysis is to investigate the predictors of placebo response in intra-articular injection trials in OA. IPD meta-analysis is considered to be superior to conventional meta-analysis, as it combines multiple trial data, facilitates the standardisation of analyses across different studies and allows measuring derivation of the desired information. Method and analysis A systematic literature search will be conducted for randomised clinical trials comparing corticosteroid and viscosupplementation/hyaluronic acid intra-articular injections with placebo for knee and hip OA. Pubmed (Medline), EMBASE, Web of Science, Cochrane Central and SCOPUS will be searched from inception to September 2018. Corresponding authors of the original trials will be contacted to obtain IPD. Risk of bias will be assessed using the Cochrane Collaboration’s tool. The primary outcome will be change in pain from baseline. Secondary outcomes will be change in function and patient’s global assessment. Potential predictors of placebo response assessed will include patient’s characteristics, pain mechanism characteristics, radiographic severity, pain severity, intervention characteristics and trial design characteristics. A multilevel logistic regression analyses will be applied. Results will be reported using the Preferred Reporting Items for Systematic review and Meta-Analysis -IPD guidelines. Ethics and dissemination This study does not include identifiable data and ethical approval was obtained by the original investigators. Results of the IPD meta-analysis will be disseminated for publication in peer-reviewed journals and conference presentations

Predictors of placebo response to local (intra-articular) therapy in osteoarthritis: An individual patient data meta-analysis protocol

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. Introduction Osteoarthritis (OA) is a highly prevalent and disabling condition with limited safe and effective treatment options. Intra-articular therapies are increasingly being used, however whether the effect of these agents is due to active treatment or placebo remains unclear. As the placebo response can be attributed to multiple factors, assessment of the placebo response using individual patient data (IPD) meta-analysis will give insight into the different modifiers of response to placebo. The aim of this IPD meta-analysis is to investigate the predictors of placebo response in intra-articular injection trials in OA. IPD meta-analysis is considered to be superior to conventional meta-analysis, as it combines multiple trial data, facilitates the standardisation of analyses across different studies and allows measuring derivation of the desired information. Method and analysis A systematic literature search will be conducted for randomised clinical trials comparing corticosteroid and viscosupplementation/hyaluronic acid intra-articular injections with placebo for knee and hip OA. Pubmed (Medline), EMBASE, Web of Science, Cochrane Central and SCOPUS will be searched from inception to September 2018. Corresponding authors of the original trials will be contacted to obtain IPD. Risk of bias will be assessed using the Cochrane Collaboration's tool. The primary outcome will be change in pain from baseline. Secondary outcomes will be change in function and patient's global assessment. Potential predictors of placebo response assessed will include patient's characteristics, pain mechanism characteristics, radiographic severity, pain severity, intervention characteristics and trial design characteristics. A multilevel logistic regression analyses will be applied. Results will be reported using the Preferred Reporting Items for Systematic review and Meta-Analysis -IPD guidelines. Ethics and dissemination This study does not include identifiable data and ethical approval was obtained by the original investigators. Results of the IPD meta-analysis will be disseminated for publication in peer-reviewed journals and conference presentations. PROSPERO registration number CRD4201809518

A consensus-based framework for conducting and reporting osteoarthritis phenotype research

Background The concept of osteoarthritis (OA) heterogeneity is evolving and gaining renewed interest. According to this concept, distinct subtypes of OA need to be defined that will likely require recognition in research design and different approaches to clinical management. Although seemingly plausible, a wide range of views exist on how best to operationalize this concept. The current project aimed to provide consensus-based definitions and recommendations that together create a framework for conducting and reporting OA phenotype research. Methods A panel of 25 members with expertise in OA phenotype research was composed. First, panel members participated in an online Delphi exercise to provide a number of basic definitions and statements relating to OA phenotypes and OA phenotype research. Second, panel members provided input on a set of recommendations for reporting on OA phenotype studies. Results Four Delphi rounds were required to achieve sufficient agreement on 11 definitions and statements. OA phenotypes were defined as subtypes of OA that share distinct underlying pathobiological and pain mechanisms and their structural and functional consequences. Reporting recommendations pertaining to the study characteristics, study population, data collection, statistical analysis, and appraisal of OA phenotype studies were provided. Conclusions This study provides a number of consensus-based definitions and recommendations relating to OA phenotypes. The resulting framework is intended to facilitate research on OA phenotypes and increase combined efforts to develop effective OA phenotype classification. Success in this endeavor will hopefully translate into more effective, differentiated OA management that will benefit a multitude of OA patients

Archetypal autophagic players through new lenses for bone marrow stem/mature cells regulation

The bone marrow landscape consists of specialized and stem/progenitor cells, which coordinate important tissue-related and systemic physiological features. Within the marrow cavity, stem/progenitor and differentiated hematopoietic and skeletal cells congregate into dynamic functional assemblies throughout specific anatomical regions, termed niches. There is a need for better understanding of the bone marrow microareas, through exploration of the intramural physical and molecular interactions of the distinctive cell populations. The elective liaisons established among the mesenchymal/stromal stem cell and hematopoietic stem cell lineage trees play a key role in orchestrating the stem/mature cell behavior and customized hierarchies within bone marrow cell populations. Recently, the autophagic apparatus has been discovered to be an important feature of bone marrow homeostasis. Autophagy-related factors involved in the labyrinthic and highly dynamic bone marrow workshop redesign the niche framework by coordinating the operational schedule of pluripotent stem and mature cells. The following report summarizes the most recent breakthroughs in our understanding of the intramural relationships between bone marrow cells and key autophagic mediators. Doubtless, the consideration of the autophagy-related and unrelated functions of main players, such as p62, Atg7, Atg5, and Beclin-1 remains a compelling task to thoroughly understand the complex relations between the heterogenic cell types that populate bone marrow

Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis : development of a prediction model to identify progressors

OBJECTIVE: There is significant variability in the trajectory of structural progression across people with knee osteoarthritis (OA). We aimed to identify distinct trajectories of femorotibial cartilage thickness over 2 years and develop a prediction model to identify individuals experiencing progressive cartilage loss. METHODS: We analysed data from the Osteoarthritis Initiative (OAI) (n = 1,014). Latent class growth analysis (LCGA) was used to identify trajectories of medial femorotibial cartilage thickness assessed on magnetic resonance imaging (MRI) at baseline, 1 and 2 years. Baseline characteristics were compared between trajectory-based subgroups and a prediction model was developed including those with frequent knee symptoms at baseline (n = 686). To examine clinical relevance of the trajectories, we assessed their association with concurrent changes in knee pain and incidence of total knee replacement (TKR) over 4 years. RESULTS: The optimal model identified three distinct trajectories: (1) stable (87.7% of the population, mean change -0.08 mm, SD 0.19); (2) moderate cartilage loss (10.0%, -0.75 mm, SD 0.16) and (3) substantial cartilage loss (2.2%, -1.38 mm, SD 0.23). Higher Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC) pain scores, family history of TKR, obesity, radiographic medial joint space narrowing (JSN) ≥1 and pain duration ≤1 year were predictive of belonging to either the moderate or substantial cartilage loss trajectory [area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.74, 0.84]. The two progression trajectories combined were associated with pain progression (OR 1.99, 95% CI 1.34, 2.97) and incidence of TKR (OR 4.34, 1.62, 11.62). CONCLUSIONS: A minority of individuals follow a progressive cartilage loss trajectory which was strongly associated with poorer clinical outcomes. If externally validated, the prediction model may help to select individuals who may benefit from cartilage-targeted therapies