Osteoarthritis (OA) is common musculoskeletal disease with increased incidence and prevalence associated with aging. OA is a major cause of disability and impaired quality of life. Although the disease presents a significant burden to patients, most do not receive the correct therapies or treatments1. Osteoarthritis trials have relatively a rate of failure due to many factors, including disease heterogeneity, a disconnect between pain improvement and structural improvement, a strong placebo response, and high variability/inaccuracy in patient reporting of pain 2. The placebo response has been estimated to comprise approximately 44-68 % of the measured treatment effect in OA and other pain-related clinical trials3,4. In RCTs, the amelioration of perceived pain by patients due to the placebo response can be so great that some experts have suggested that osteoarthritis patients may benefit from administration of placebo as a treatment option5.
While the placebo effect is a significant issue in clinical trials using pain as an endpoint, inaccuracy in pain symptom reporting is also a significant issue. Pain intensity is a subjective phenomenon involving pathophysiological and biopsychosocial factors. In OA RCTs, most endpoints rely on patient reporting of pain using standard scales or questionnaires. If some scales (numerical rating scale (NRS), visual analog scale (VAS), and verbal rating scale (VRS)) have excellent test–retest reliability6. However, a substantial number of OA patients have high pain reporting variability7,8 that can be linked to fluctuation in symptoms1. On one hand, the exclusion of patients with a highly variable baseline was suggested to improve the assay sensitivity of clinical trials9 – but this strategy can extend clinical trial recruitment and thus increase cost and timeline. On the other hand, better understanding intraindividual variability of pain may provide a new perspective for understanding the complex mechanisms of pain and optimizing pain assessment in drug development10.
The Cognivia team recently investigated the influence of the error in patient reporting of pain intensity to increase the quality of study data11. The analysis estimated variability in pain reporting and its evolution over time in 64 OA subjects (hip and knee) from a multicenter, multi-country study in which patients were given placebo for 12 weeks in a blinded manner. Patients were asked to assess their average pain score (APS), worst pain score (WPS) and lowest pain score (LPS) daily from the initial screening visit through the final end-of-study visit. We analyzed the learning effect resulting from the daily repetition of these 3 assessments. At the five clinic visits, they were also asked to complete the short form of the Brief Pain Inventory.
The results showed a reduction over time of the evaluation error (adjusted auto-correlation) by around 50 % of daily pain measurements, where subjects reported pain intensity more consistently at the end of the study. Based on the results of these analyses, a baseline period of 20-40 days for daily self-recording of pain assessment seemed to achieve a meaningful and significant reduction of pain evaluation error.
These data suggest that patients may experience a learning effect in pain reporting after daily self-assessment. Traditionally, pain reporting errors are addressed by training patients a priori for accurate and consistent symptom assessment. These data suggest that the learning effect resulting from repeated, self-reporting of pain over time may be a convenient and cost-effective alternative to patient training to improve pain reporting consistency. These results complement Cognivia’s platform solution (Placebell©™) to reduce the impact of the placebo response in clinical trials.
These data were presented in a poster titled “Can daily self-assessment induce a learning effect mitigating pain evaluation error in clinical trials?” at the OARSI Connect 2021 Virtual World Congress on Osteoarthritis, April 29 – May 1, 2021. Click here for the poster abstract and video poster presentation and contact us for more information.
References:
1. Hunter, D. J. & Bierma-Zeinstra, S. Osteoarthritis. The Lancet 393, 1745–1759 (2019).
2. Felson, D. T. & Neogi, T. Emerging Treatment Models in Rheumatology: Challenges for Osteoarthritis Trials. Arthritis & Rheumatology 70, (2018).
3. Huang, Z. et al. Meta-analysis of pain and function placebo responses in pharmacological osteoarthritis trials. Arthritis Research and Therapy vol. 21 (2019).
4. Häuser, W., Bartram-Wunn, E., Bartram, C., Reinecke, H. & Tölle, T. Systematic review: Placebo response in drug trials of fibromyalgia syndrome and painful peripheral diabetic neuropathy – Magnitude and patient-related predictors. Pain 152, 1709–1717 (2011).
5. Gregori, D. et al. Association of Pharmacological Treatments with Long-term Pain Control in Patients with Knee Osteoarthritis: A Systematic Review and Meta-analysis. in JAMA – Journal of the American Medical Association vol. 320 2564–2579 (American Medical Association, 2018).
6. Doherty, M. & Dieppe, P. The “placebo” response in osteoarthritis and its implications for clinical practice. Osteoarthritis and Cartilage 17, (2009).
7. Frampton, C. L. & Hughes-Webb, P. The Measurement of Pain. Clinical Oncology 23, (2011).
8. Alghadir, A., Anwer, S., Iqbal, A. & Iqbal, Z. Test-retest reliability, validity, and minimum detectable change of visual analog, numerical rating, and verbal rating scales for measurement of osteoarthritic knee pain. Journal of Pain Research Volume 11, (2018).
9. Schneider, R. & Kuhl, J. Placebo forte: Ways to maximize unspecific treatment effects. Medical Hypotheses 78, 744–751 (2012).
10. Parry, E., Ogollah, R. & Peat, G. Significant pain variability in persons with, or at high risk of, knee osteoarthritis: preliminary investigation based on secondary analysis of cohort data. BMC Musculoskeletal Disorders 18, (2017).
11. Ooghe, A., Branders, S. & Pereira, A. Can daily self-assessment induce a learning effect mitigating pain evaluation error in clinical trials? Osteoarthritis and Cartilage 29, (2021).
