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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.

Objective: To evaluate the efficacy of intra--articular (IA) glucocorticoid for knee or hip osteoarthritis (OA) in specific subgroups of patients according to the baseline severity of pain and inflammatory signs using individual patient data (IPD) from existing trials. Furthermore, this study aims to assess if a baseline pain cut-off was associated with clinically important effectiveness of IA glucocorticoid. This is an update of an IA glucocorticoid IPD meta-analysis by the OA Trial Bank. Method: Randomized trials evaluating one or more IA glucocorticoid preparations in hip and knee OA, published to May 2018 were selected. IPD of patient and disease characteristics and outcome measures were acquired. The primary outcome was pain severity at short-term follow-up (up to 4 weeks). Potential interaction effect of severe pain (≥70 points, 0–100 scale) and signs of inflammation at baseline were studied using a two-stage approach with general liner model followed by random effects model. Analysis of trend was conducted, assessing if a baseline pain cut-off was associated with the threshold for clinically important treatment effect of IA glucocorticoid compared to placebo. Results: Four out of 16 eligible randomized clinical trials (n ​= ​641) were combined with the existing OA Trial Bank studies (n ​= ​620), yielding 1261 participants from eleven studies. Participants with severe baseline pain compared to those with less severe pain had greater pain reduction at mid-term (around 12 weeks) (mean reduction: −6.90 (95%CI -10.91; −2.90)), but not at short- and long-term. No interaction effects were found between inflammatory signs and IA glucocorticoid injections compared to placebo at all follow-up time-points. Analysis of trend demonstrated treatment response to IA glucocorticoid from baseline pain levels >50 (0–100 scale) and above. Conclusion: This updated IPD meta-analysis demonstrated that participants with severe pain compared to those with less severe pain at ba

ObjectiveTo determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.MethodsThe present study was a nested case-control trial from the Foundation of the National Institutes of Health OA Biomarkers Consortium that assessed study participants with a Kellgren/Lawrence grade of 1-3 who had complete biomarker data available (n = 539 to 550). Cases were participants' knees that had radiographic and pain progression between 24 and 48 months compared to baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) that had a P value of <0.10 in univariate analysis were selected, including quantitative cartilage thickness and volume on magnetic resonance imaging (MRI), semiquantitative MRI markers, bone shape and area, quantitative meniscal volume, radiographic progression (trabecular bone texture [TBT]), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using 3 different stepwise selection methods (complex models versus parsimonious models).ResultsAmong baseline biomarkers, the number of locations affected by osteophytes (semiquantitative), quantitative central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and semiquantitative Hoffa-synovitis predicted OA progression in most models (C statistic 0.641-0.671). In most models, 24-month changes in semiquantitative MRI markers (effusion-synovitis, meniscal morphologic changes, and cartilage damage), quantitative central medial femoral cartilage thickness, quantitative medial tibial cartilage volume, quantitative lateral patellofemoral bone area, horizontal TBT (intercept term), and urine N-telopeptide of type I collagen predicted OA progression (C statistic 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, whic

OBJECTIVE: To determine the optimal combination of imaging and biochemical biomarkers to predict knee osteoarthritis (OA) progression.METHODS: Nested case-control study from the FNIH OA Biomarkers Consortium of participants with Kellgren-Lawrence grade 1-3 and complete biomarker data (n=539 to 550). Cases were knees with radiographic and pain progression between 24-48 months from baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) with p<0.10 in univariate analysis were selected, including MRI (quantitative (Q) cartilage thickness and volume; semi-quantitative (SQ) MRI markers; bone shape and area; Q meniscal volume), radiographic (trabecular bone texture (TBT)), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using three different step-wise selection methods (complex vs. parsimonious models).RESULTS: Among baseline biomarkers, the number of locations affected by osteophytes (SQ), Q central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and SQ Hoffa-synovitis predicted progression in most models (C-statistics 0.641-0.671). 24-month changes in SQ MRI markers (effusion-synovitis, meniscal morphology, and cartilage damage), Q central medial femoral cartilage thickness, Q medial tibial cartilage volume, Q lateral patellofemoral bone area, horizontal TBT (intercept term), and urine NTX-I predicted progression in most models (C-statistics 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, with higher C-statistics (0.716-0.832).CONCLUSION: This study highlights the combination of biomarkers with potential prognostic utility in OA disease-modifying trials. Properly qualified, these biomarkers could be used to enrich future trials with participants likely to progress.

OBJECTIVE: To determine the optimal combination of imaging and biochemical biomarkers to predict knee osteoarthritis (OA) progression.METHODS: Nested case-control study from the FNIH OA Biomarkers Consortium of participants with Kellgren-Lawrence grade 1-3 and complete biomarker data (n=539 to 550). Cases were knees with radiographic and pain progression between 24-48 months from baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) with p<0.10 in univariate analysis were selected, including MRI (quantitative (Q) cartilage thickness and volume; semi-quantitative (SQ) MRI markers; bone shape and area; Q meniscal volume), radiographic (trabecular bone texture (TBT)), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using three different step-wise selection methods (complex vs. parsimonious models).RESULTS: Among baseline biomarkers, the number of locations affected by osteophytes (SQ), Q central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and SQ Hoffa-synovitis predicted progression in most models (C-statistics 0.641-0.671). 24-month changes in SQ MRI markers (effusion-synovitis, meniscal morphology, and cartilage damage), Q central medial femoral cartilage thickness, Q medial tibial cartilage volume, Q lateral patellofemoral bone area, horizontal TBT (intercept term), and urine NTX-I predicted progression in most models (C-statistics 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, with higher C-statistics (0.716-0.832).CONCLUSION: This study highlights the combination of biomarkers with potential prognostic utility in OA disease-modifying trials. Properly qualified, these biomarkers could be used to enrich future trials with participants likely to progress.