(M-042) Leveraging Real-World Data for Osteoarthritis Severity Score Modeling: An InSilicoTrials Platform Application
Monday, October 20, 2025
7:00 AM - 5:00 PM MDT
Location: Colorado A
Paolo Messina – InsilicoTrials; Giuseppe Pasculli – InsilicoTrials; Marco Virgolin – InsilicoTrials; Alessio Farcomeni – Department of Economics and Finance – University of Rome "Tor Vergata", Rome, Italy; Pauline Bambury – InsilicoTrials; Jane Knöchel – InsilicoTrials; Maud Beneton – InsilicoTrials; Daniel Roeshammar – InsilicoTrials
Objectives: To model and quantify knee osteoarthritis (KOA) pain severity using real-world data (RWD), and to identify factors influencing pain trajectories relevant for pharmacological intervention, patient stratification, and clinical trial design. This study employed the InSilicoTrials platform to integrate RWD analytics, statistical modelling, and clinical trial simulation to support precision pharmacology and data-driven decisions in drug development.
Methods: RWD included 85.5k records from 15.7k patients across Medicare, Medicaid, and commercial claims databases in the US, 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 absence of direct pain severity measures, a novel time-dependent Poisson process was used to develop a dynamic severity score (SS), with each healthcare encounter contributing to a transient increase in pain signal followed by exponential decay (Daley & Vere-Jones, 2003, 2009).
A linear mixed model (LMM) with repeated measures assessed relationships between SS and patient characteristics, with random intercepts accounting for inter-individual variability. 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: SS increased by 9% per 10-year age increment (p < 0.01), and male patients showed an 11% lower SS compared to females (p < 0.01). Joint replacement and stiffness were each associated with 17% higher SS, while swelling and trauma led to increases of 7% and 5%, respectively (p < 0.01). Each additional inpatient day resulted in a 3% rise in SS (p = 0.03). Pharmacologic treatment was associated with substantial SS reductions: NSAIDs (-53%), opioids (-42%), all reached statistical significance (all p < 0.01). Routes of administration also contributed to pain reduction, with topical, oral, and other routes decreased SS by 57%, 51%, and 51%, respectively (all p < 0.01). Among specific treatments, steroids, diclofenac sodium, and naproxen were linked to>60% SS reduction (p < 0.01), while gabapentin and oxycodone showed non-significant changes. NSAIDs use typically lasted two weeks, opioids regimens averaged one month. Among initially untreated patients, 20.5% initiated NSAIDs and 7.0% initiated opioids, with average time-to-treatment of 7.3 and 9.0 months, respectively.
Conclusions: This study shows how large-scale RWD can reveal key insights into KOA pain progression and treatment response. The modelling approach supports improved characterization of disease burden and drug effects, critical for clinical pharmacology applications such as dose optimization, endpoint refinement, and patient targeting. The InSilicoTrials platform enabled efficient modelling and simulation, offering a scalable tool for optimizing trial design and supporting informed decision-making across osteoarthritis drug development.
Citations: 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.
