Update of individual data meta-analysis of intra-articular corticosteroids in knee and hip osteoarthritis

Lead Investigator: Shirley Yu, University of Sydney
Title of Proposal Research: Update of individual data meta-analysis of intra-articular corticosteroids in knee and hip osteoarthritis
Vivli Data Request: 7654
Funding Source: Dr Shirley Yu holds a University of Sydney Postgraduate Research Scholarship
Potential Conflicts of Interest: Professor David Hunter provides consulting advice to Merck Serono, Pfizer, Lilly, TLCBio, Kolon Tissuegene. The potential conflicts of interest will be managed through disclosure of interests when the research is presented and published.

Summary of the Proposed Research:

Osteoarthritis (OA) is a highly prevalent and disabling condition with limited safe and effective treatment options available. Intra-articular corticosteroid injections are frequently utilised in knee or hip OA patients in the setting of unresponsiveness or intolerance to conservative treatment measures or common pharmacological agents such as non-steroidal anti-inflammatory drugs.
The aim of this individual patient data meta-analysis is to investigate the efficacy of intra-articular corticosteroid injections in patients with hip and knee OA.

Method and analysis:
A systematic literature search will be conducted for randomised clinical trials in corticosteroid intra-articular injection with placebo for knee and hip osteoarthritis. Literature searches will be conducted through Pubmed (Medline), EMBASE, Web of Science, Cochrane Central, and SCOPUS. Individual patient data from each study will be requested and obtained from the corresponding authors of the trials. Risk of bias will be assessed using the Cochrane Collaboration’s tool.

The primary outcome will be pain severity at short term with secondary outcomes being pain severity at other time points

Statistical Analysis Plan:

Overall effects between the different comparative treatments and within these comparisons will be estimated in the pooled IPD. Descriptive comparisons between studies will be conducted to assess between-study differences. We assume the data to be missing at random (MAR), therefore, observed patient characteristics will be used to impute missing data (potential co-variates and outcomes), by means of multiple imputation.5 6 Missing data will be imputed within each original study, before data of the individual studies are pooled. Treatment effects will be analysed using a random effects model. The heterogeneity between the separate trials will be tested with I-square. An additional analysis will be performed by excluding the trials causing
heterogeneity in order to reach an I-square index of at least below 50.
The analyses will be adjusted for variables used in stratified randomisation procedures when necessary.

The primary outcome is pain severity at short-term follow-up. When pain severity is measured on different scales, we will standardize these pain scores in order to pool the data. Secondary outcomes include pain severity assessed at other follow-up duration, physical functioning and
global assessment.

The subgroup factor will, based on consensus, be standardised to a) yes or no severe pain and b) yes or no signs of mild inflammation and yes or no signs of moderate to severe inflammation. In addition, separate pooled analysis, if possible, will take place for the different assessments to
define inflammation, and for hip and knee OA.

One-step approach:
Subgroup analyses will be performed by including a single covariate in the regression model to indicate the study in order to adjust for possible residual confounding by study differences. Other covariates define possible confounders that can be distributed unequally over the treatments in
subgroups. At least age, gender and BMI will be included, but if possible also duration of complaints, radiographic severity and educational level will be included.

To assess potential subgroup effects, a fixed-effects linear regression model will be used to calculate interaction effects. The model will include the dependent variable, i.e. pain intensity at follow-up (0-100pt scale), independent variables, i.e. treatment (corticosteroid injection or
control), the effect modifier (severe pain (yes or no) and signs of inflammation (yes or no)), and an interaction term (pain BY treatment or inflammation BY treatment). Interaction effect with p-value less than 0.05 will be considered as statistically significant. The clinical significance of the interaction effect will be estimated by the effect size.

Requested Studies:

Comparison of the Efficacy and Safety of Hylastan to Methylprednisolone Acetate in Patients With Symptomatic Osteoarthritis of the Knee
Data Contributor: Sanofi
Study ID: NCT00139295
Sponsor ID: AVS00103

Public Disclosures:

  1. Shirley PY, van Middelkoop M, Ferreira ML, Deveza L, Bierma-Zeinstra SM, Venkatesha V, Hunter DJ. The OA Trial Bank: Update of individual patient data meta-analysis of intra-articular glucocorticoids in persons with knee and hip osteoarthritis. Osteoarthritis and Cartilage Open. 2023 Apr 11:100362. doi: 10.1016/j.ocarto.2023.100362
  2. Yu, S., Van Middelkoop, M., Ferreira, M., Deveza, L., Bierma-Zeinstra, S.M.A., Venkatesha, V. and Hunter, D., 2023. POS1362 THE OA TRIAL BANK: UPDATE OF INDIVIDUAL PATIENT DATA META-ANALYSIS OF INTRA-ARTICULAR GLUCOCORTICOIDS IN PERSONS WITH KNEE AND HIP OSTEOARTHRITIS. Doi: 10.1136/annrheumdis-2023-eular.1812