Paolo Messina1, Giuseppe Pasculli1, Marco Virgolin2, Alessio Farcomeni3, Pauline Bambury1, Jane Knöchel1, Maud Beneton1, Daniel Roeshammar1
1InSilicoTrials Technologies S.p.A., 2InSilicoTrials Technologies B.V, 3Department of Economics and Finance, University of Rome
Introduction: Knee osteoarthritis (KOA) is a leading cause of disability, with intermittent flare-ups significantly impacting patient quality of life. Accurate assessment of pain severity and flare-up patterns is crucial for optimizing disease management strategies. Understanding the factors that influence pain severity can improve decision-making in drug development and when initiating drug therapy. Objectives: This study aimed to identify the factors influencing knee pain severity in patients with osteoarthritis, leveraging real-world data (RWD) to inform drug development processes. Analyses were performed using the InSilicoTrials platform, which integrates advanced statistical methods, clinical trial simulations, and the visualization of patient outcomes, to provide a comprehensive view of treatment effects and patient characteristics. Methods: RWD included 85.5k records from 15.7k patients across Medicare, Medicaid, and commercial claims databases in the U.S., with access spanning from January 2017 to December 2018. Patients were identified based on specific ICD-10-CM codes for knee pain (M17.9, M17.12, M25.561, M25.562, M25.461, M76.31). Given the lack of a direct measure for pain severity, a pain severity score (SS) was developed using a time-dependent Poisson process that quantified pain based on healthcare utilization patterns (Daley and Vere-Jones, 2003, 2009). Each new healthcare encounter contributed to an increase in pain severity followed by exponential decay. A linear mixed model (LMM) with repeated measures was applied to validate the pain severity measure. The model assessed associations between SS and clinical factors, accounting for patient variability with a random intercept. Fixed effects included demographics (age, gender), clinical characteristics (obesity, insomnia, depression, stiffness, swelling, trauma, joint replacement), encounter characteristics (type of visit, length of stay), and medication use (drug class, dose, route of administration). All analyses were performed using R software (Version 4.3.0) within the InSilicoTrials platform. Results: For every 10-year increase in age, SS increased by 9% (p < 0.01), while male patients experienced an 11% reduction in SS compared to females (p < 0.01). Patients who underwent joint replacement showed a 17% increase in SS. Stiffness was associated with a 17% increase, while swelling and trauma were associated with increases in pain severity of 7% and 5%, respectively (all p < 0.01). For each additional day in the hospital, SS increased by 3% (p = 0.03). The use of medications was strongly linked to a reduction in SS: NSAIDs, opioids, and other drug classes led to reductions of 53%, 42%, and 47%, respectively (all p < 0.01). Routes of administration also contributed to pain reduction, with topical, oral, and other routes associated with SS reductions of 57%, 51%, and 51%, respectively (all p < 0.01). When focusing on treatment classes, steroids, diclofenac sodium, and naproxen were those associated with more than 60% reduction in SS at p < 0.01, while gabapentin and oxycodone showed a non-significant reduction in SS when compared to no treatment. When initiated, NSAIDs treatment usually lasted two weeks before suspending treatment, while opioids treatment usually took longer (one month). Patients who did not receive any treatment at their first visit transitioned to NSAIDs and opioids respectively in 20.5% and 7.0% of cases, with an average time to initiation of 7.3 and 9.0 months, respectively. Conclusions: This study highlights the critical role of RWD in providing valuable insights into assessing the key components of KOA pain across a large cohort of patients. The observed patterns in treatment initiation, switching behaviour, and pain reduction dynamics offer a foundation for designing more targeted clinical trials. By leveraging these real-world insights, future studies can optimize trial design, ensuring precise sample size estimation and improving statistical power. The InSilicoTrials platform provides a robust framework to simulate and refine study parameters, facilitating data-driven decision-making in drug development and enhancing the precision of treatment effect assessments.
Daley, D.J., Vere-Jones, D., 2003. An Introduction to the Theory of Point Processes. Volume I: Elementary Theory and Methods. Springer-Verlag, New York. Vere-Jones, D., 2009. Some models and procedures for space-time point processes. Environ. Ecol. Stat. 16, 173–195.
Reference: PAGE 33 (2025) Abstr 11572 [www.page-meeting.org/?abstract=11572]
Poster: Methodology - Model Evaluation