Investigating predictors of pain events during trials of knee osteoarthritis: A post-hoc analysis of two phase III trials
Abstract
Objective: This study aims to explore the associations between clinical characteristics, including medical history, and the frequency, severity, and type of pain events (PEs) over a two-year period in patients with knee osteoarthritis (OA).
Design: A post-hoc analysis was conducted on data from two double-blinded, randomized, placebo-controlled, multicenter phase-III trials assessing the efficacy and safety of salmon calcitonin in patients with painful and radiographic knee OA. 2206 patients aged 51-80 years were included. Adverse events (AEs) were recorded and categorized into PEs and non-PEs. Logistic regression models were used to assess associations, adjusting for confounders such as age, sex, and body mass index (BMI).
Results: Of the 8183 AEs recorded, 27.6 % were classified as PEs. Higher BMI was associated with lower odds of reporting PEs (OR: 0.94, p = 0.02), while male patients reported fewer PEs than females (OR: 0.83, p = 0.04). Insomnia and depression were linked to increased odds of PEs. In the PE population, higher BMI was associated with increased odds of moderate and severe PEs. KL grade 3 was linked to lower odds of moderate and severe PEs.
Conclusions: The study identifies female sex and certain medical histories as significant predictors of PEs in knee OA patients. A higher BMI was associated with lower odds of reporting PEs but for those who reported PEs, higher BMI was associated with more severe PEs. These results should be interpreted with caution due…
How strong is the evidence?
Evidence Hub reads this as a secondary / pooled analysis, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 2,206 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.