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Implant density reduction to avoid proximal junctional failure in adult spine surgery: Computer models and
Morteza Rasouligandomani1, Alex Del Arco2,3, Tomaso Villa4
1BCN MedTech, Department of Engineering, DTIC, University Pompeu Fabra, Barcelona, Spain.
Background:
Proximal Junctional Failure (PJF) is a common complication in Adult Spine Deformity (ASD) surgeries, often leading to reoperations. While revision surgeries with osteotomies carry high complication rate of 34.8%, alternatives such as hardware proximal extension may increase PJF risk in patients with severe Global Alignment and Proportion (GAP) scores. Implant Density Reduction (IDR) has emerged to mitigate PJF risk. This study assessed the impact of IDR on PJF risk and explored sub-optimal strategies.
Methods:
Two patient-personalized Finite Element (FE) models were used and expanded into a virtual cohort. Implant Density (ID), rod material, bone quality, and GAP were systematically varied. Thoracolumbar FE models were developed using structured Statistical Shape Modeling (SSM). Biomechanical metrics of Intervertebral Disk (IVD) fiber strain, Screw Pull-out Force (SPF), and rod stress, were evaluated. Trade-off analyses could determine sub-optimal configurations avoiding PJF.
Results:
IDR significantly decreased IVD strain (up to -70%) and improved screw stability (up to +142%), for patients with titanium (Ti) rods and normal bone. However, IDR effectiveness was limited for cases with GAP ≥12, osteoporotic bone, and Cobalt-Chromium (Cr-Co) rods. No IDR strategy could prevent PJF for cases with GAP 12 or 13, regardless of rod type. For cases with GAP 11 and Upper Instrumented Vertebra (UIV) at T10, IDR was effective with only Ti rods. For cases with GAP 13 and UIV at T3, none of IDRs, independent of rod material, offered benefit. Notably, Ti rods may support IDR-based risk reduction in borderline cases, such as GAP 12, UIV at T3.
Conclusions:
IDR is a promising strategy to lower PJF risk in high-risk spine revision cases, though its effectiveness depends on surgical and anatomical factors. This study provides an in-silico tool to support personalized surgical planning and guide future clinical trials aimed at reducing reoperations and healthcare costs.
