The Role of Biological Age in Adult Spinal Deformity Surgery

➢ Adult spinal deformity (ASD) outcomes are poorly predicted by chronological age alone; biological age markers, including frailty indices, sarcopenia measurements, and bone quality metrics, provide better risk assessment. ➢ Systemic frailty tools (modified Frailty Index, ASD-Frailty Index [ASD-FI], Risk Analysis Index) predict perioperative morbidity and discharge disposition more accurately than age-based models do. ➢ Musculoskeletal assessments, psoas and paraspinal muscle cross-sectional areas, fatty infiltration grading, and computed tomography-derived Hounsfield unit thresholds identify patients with sarcopenia and osteoporosis at elevated risk for proximal junctional failure, implant fracture, and pseudarthrosis. ➢ Artificial intelligence and machine learning can automate osteosarcopenia quantification and define pelvic incidence-dependent alignment phenotypes, which may personalize correction targets. ➢ Integrating multidimensional biological age profiles into preoperative planning facilitates tailored surgical aggressiveness, age-appropriate and morphology-appropriate alignment goals, and targeted prophylactic strategies, paving the way toward a physiology-driven model in ASD surgery.