BACKGROUND: Adult spinal deformity (ASD) is an increasingly prevalent disorder in the aging population. Surgical intervention is a common and generally effective treatment for severe cases. However, it is associated with relatively high rates of complications, one of the most common, and devastating of which is proximal junctional failure (PJF). PJF is characterized by symptomatic mechanical failure at the junction of the spinal fusion construct and the adjacent proximal mobile spinal segments, leading to a kyphotic deformity.

CURRENT LIMITATIONS: The etiology of PJF remains a topic of ongoing investigation, with uncertainty surrounding the specific factors that predispose individual patients to this complication. Current predictive models primarily rely on radiographic parameters on standing X-rays to assess PJF risk, but their clinical utility remains limited. We contend that these models universally fail to adequately account for the role of paraspinal muscle function and dysfunction, iatrogenic surgical muscle injury, bone quality, integrity of the discoligamentous elements, and spinal kinetics.

PROPOSED APPROACH: Musculoskeletal modeling offers a powerful tool to enhance our understanding of human body kinetics and kinematics, including the complex biomechanical interactions in the spine. By integrating the biomechanical characteristics of bone and soft tissue into surgical treatment planning, we contend that subject-specific musculoskeletal modeling will improve PJF predictability, enable the explanation and interpretation of PJF, and ultimately optimize outcomes for patients undergoing surgery for ASD.

CONCLUSION: Subject-specific musculoskeletal modeling represents a critical opportunity to address the limitations of existing predictive systems and advance the field of ASD management.