Epidemiological-Inspired Model of Post-Operational Chronic Pain in Scoliosis Patients

Abstract

Chronic post-surgical pain (CPSP) is a common and often overlooked complication following surgical correction of idiopathic scoliosis, impacting long-term patient wellbeing despite improvements in surgical outcomes. This project introduces a novel epidemiological framework to model the progression of CPSP using a Susceptible-Infected-Treatment-Recovered-Chronic Pain (SITR-C) compartmental structure. By applying a coupled system of nonlinear differential equations, we simulate pain trajectories over time and assess the effectiveness of surgical interventions. The model is implemented for a single-cohort population and extended to a two-cohort design to compare outcomes between Posterior Spinal Fusion and Vertebral Body Tethering procedures. Further stratification by patient age enables us to explore demographic influences on chronic pain dynamics. Stability analysis confirms the existence of disease-free equilibria, while numerical simulations provide insights into long-term recovery versus chronic outcomes. This work highlights the importance of integrating mathematical modeling into clinical decision-making and pain management strategies. In alignment with the United Nations Sustainable Development Goal 3: Good Health and Wellbeing, the SITR-C model offers a data-driven approach to improve patient care, predict recovery paths, and reduce the long-term burden of CPSP.