Patient-specific statistical shape modeling for optimal spinal sagittal alignment in lumbar spinal fusion

Area of Science:

• Spine surgery
• Biomechanical analysis
• Radiographic assessment

Background:

• Adjacent segment disease (ASD) is a complication following spinal fusion.
• Understanding spinal alignment deviations is crucial for preventing ASD.
• Patient-specific statistical shape modeling (SSM) offers a novel approach to analyze spinal alignment.

Purpose of the Study:

• To compare spinal alignment differences between patients who developed ASD after L4/5 spinal fusion and asymptomatic controls.
• To investigate the utility of patient-specific SSM in identifying alignment discrepancies associated with ASD.
• To assess the potential of SSM in preoperative planning to mitigate ASD risk.

Main Methods:

• A case-control study comparing patients with ASD (n=22) requiring revision surgery and asymptomatic controls (n=25) after L4/5 spinal fusion.
• Preoperative and postoperative lateral radiographs were analyzed using SSM to calculate optimal spinal sagittal alignment.
• The deviation from the optimal alignment was quantified by measuring the 2D distance from the SSM-calculated ideal to the actual patient position.

Main Results:

• Patients with ASD exhibited a greater postoperative mean distance from the SSM-calculated optimal alignment (86.8 mm) compared to controls (67.7 mm), though not statistically significant (p=0.119).
• A significant difference was observed in the postoperative vertical distance of the L4 segment, with the ASD group showing greater deviation (5.69 ± 3.0 mm) than controls (3.58 ± 3.5 mm, p=0.034).
• Greater alignment differences were noted in ASD patients at more cranial levels, suggesting a relationship between deviation and ASD development.

Conclusions:

• Lumbar spinal fusion patients requiring revision for ASD demonstrate significant deviations from optimal spinal sagittal alignment compared to asymptomatic individuals.
• Patient-specific SSM can identify these alignment differences, highlighting its potential role in preoperative planning.
• Further research is warranted to integrate SSM with existing indices for improved ASD risk reduction strategies.