Multicenter validation of a surgical planning tool for lumbar vertebral body tethering simulating growth modulation...

Vertebral body tethering (VBT) for lumbar curves may have wider application than for thoracic curves due to greater growth potential than thoracic spine and benefits of preserved flexibility. Predicting long-term correction remains challenging, with high revision rates and complications (14-32%) including under-/over-correction, tether breakage, adding-on. This study aimed to validate a planning tool for lumbar VBT using a patient-specific finite element model (FEM) integrating mechanobiological growth modulation as a function of preoperative skeletal maturity.

Thirty-five retrospective idiopathic scoliosis patients who underwent lumbar VBT, with or without concomitant thoracic VBT, were included. A personalized FEM calibrated to preoperative spine deformity, flexibility and weight was created using 3D radiographic reconstructions. The FEM was linked to an algorithm integrating spine growth and mechanobiological growth modulation, calibrated using preoperative Sanders score. VBT surgery was simulated to replicate immediate postoperative correction and predict two-year correction. Simulated Cobb angles, sagittal curves, and apical axial rotation were compared to actual two-year radiographic measurements.

Preoperative Cobb angles averaged 37 ± 12° (thoracic) and 48 ± 9° (thoraco-lumbar/lumbar). Immediate postoperative correction was 38 ± 15% and 59 ± 16%, with two-year corrections of 44 ± 24% and 73 ± 21%, respectively. Simulated postoperative correction was accurate within 3° (Cobb angles), while simulated 2-year outcomes were accurate within 3° (Cobb), 2° (kyphosis), 4° (lordosis), and 3° (axial rotation), showing no significant differences from reference results (p < 0.05; statistical power 90%).

The patient-specific FEM and growth modulation algorithm accurately predicted two-year correction. This tool can support preoperative planning, reduce surgeon variability, and potentially improve VBT outcomes by providing a predictive tool to help surgical planning.