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- DOI: 10.1093/rheumatology/kes368
- PMID: 23264554
- UKPMCID: 23264554
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Predictors of response to intra-articular steroid injections in knee osteoarthritis--a systematic review.
Maricar, Nasimah; Callaghan, Michael J; Felson, David T; O'Neill, Terence W
Rheumatology (Oxford, England). 2013;52(6):1022-1032.
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Full-text held externally
- DOI: 10.1093/rheumatology/kes368
- PMID: 23264554
- UKPMCID: 23264554
Abstract
Objective. IA steroid injections (IASIs) have been shown to relieve pain in knee OA and are widely used in clinical practice. There is, however, evidence of some variation in response. Knowledge of predictors of response could aid in the selection of patients for this therapy. The aim of this systematic review was to determine factors associated with response to IASI in knee OA.Methods. Medline, Embase, AMED, CINAHL, Web of Science and Cochrane Central Registers for Controlled Trials up to January 2012 were searched with additional hand searches of relevant articles. Studies included were those that involved adults diagnosed with knee OA in whom IASIs were administered and factors that predicted treatment response were investigated.Results. Eleven publications meeting these criteria were reviewed and relevant information extracted. It was not possible to pool the results because of the different predictors studied, variable outcome measures, different criteria for symptom change and missing data. Given the relative paucity of data and small heterogeneously designed studies, it was difficult to identify predictors of response. Data from individual publications, although not consistent across studies, suggest that the presence of effusion, withdrawal of fluid from the knee, severity of disease, absence of synovitis, injection delivery under US guidance and greater symptoms at baseline may all improve the likelihood of response to IASI.Conclusion. Further larger-scale studies using standardized methods are required to characterize predictors of response and should focus on synovitis, effusion, pain and structural severity of disease. Such data would help in better targeting therapy to those most likely to benefit.