Evidence-based tailored conservative treatment of knee and hip osteoarthritis: between knowing and doing

Abstract

Objective: Insufficient data are available on the efficacy of combined conservative interventions recommended by treatment guidelines for knee/hip osteoarthritis (OA). The aims of this observational cohort study were (i) to estimate the results of an evidence-based 12-week tailored multimodal conservative treatment protocol for patients with knee/hip OA and (ii) to identify predictors for response.

Methods: After obtaining data on previous OA-related interventions, multimodal treatment was offered to patients with knee and/or hip OA at a specialized outpatient clinic. Treatment with analgesics was tailored using a numeric rating scale (NRS) for pain, aiming for NRS ≤ 4. The following outcome measures were assessed: (i) the proportion of patients fulfilling OMERACT-OARSI (Outcome Measures in Rheumatoid Arthritis Clinical Trials/Osteoarthritis Research Society International) responder criteria and (ii) the proportion of patients with NRS pain ≤ 4 after 12 weeks.

Results: A total of 183 out of 299 patients was included. OMERACT-OARSI responder criteria were fulfilled at 12 weeks in 47% of patients; 39% reached NRS pain ≤ 4. The only independent predictor for response was the number of previously used non-steroidal anti-inflammatory drugs (NSAIDs). The majority of patients had not been exposed adequately to conservative treatment modalities for knee and/or hip OA in the past (81%).

Conclusion: Evidence-based multimodal conservative treatment using a standardized protocol for knee and/or hip OA is feasible and successful in 47% of patients. In general, response could not be predicted. Basic first-line recommended conservative treatment options have not been used adequately prior to referral to secondary care in the vast majority of patients.

Acknowledgements

We thank Dr DJ de Rooij for radiograph scoring, M Arts-Sanders for data collection and database building, K Defoort for referring patients, and FW Snijders, N Eikelenboom, N den Broeder, and D Jongman for data entry and management.