Project 456413

Spinal disorders as a result of aging, trauma, deformity and cancer cause significant pain and disability and often require surgical intervention for effective treatment. These reconstructive surgeries aim to restore spinal column alignment to reduce pain and regain mobility, with metallic implants used to re-establish the normal separation between the bony vertebrae. Unfortunately, the underlying bone strength is often weakened, leading to penetration, or subsidence, of the implant into the vertebral bone. This causes a loss of the corrective alignment, and in many cases pain and the need for revision surgery. Many factors contribute to this clinical problem; yet, there are currently no tools available to predict implant subsidence for a given patient. The overall goal of this project is to develop predictive risk assessment tools that can better identify and reduce patient-specific implant subsidence risk to improve outcomes of invasive spine surgery. To achieve this objective, we will first examine post-operative clinical images from patients who have received reconstructive spine surgery to identify patient and surgical factors associated with cage subsidence. We will then generate new experimental data, using microstructural imaging, to define how vertebral bone fails under different loading conditions. New experimental data will be used to enhance our existing computational model to more accurately predict vertebral bone failure, increasing the robustness of the model to better represent a wider population of patients and risk factors. Lastly, we will validate the computational model output to predict implant subsidence risk for an individual patient. Collectively, the project objectives will result in predictive risk assessment tools that can identify patients at risk of cage subsidence and pre-operatively inform the surgeon to avoid costly and unnecessary implant use.