Improving the accuracy of current sagittal alignment evaluation system centered around pelvic incidence: a new...

Area of Science:

• Orthopedics
• Radiology
• Biomechanical Engineering

Background:

• Spinopelvic alignment is crucial for maintaining sagittal balance.
• Variations in alignment can impact spinal health and surgical outcomes.
• A standardized classification and predictive models are needed for accurate assessment.

Purpose of the Study:

• To characterize spinopelvic alignment variations in asymptomatic individuals.
• To develop a classification system for spinopelvic alignment.
• To create predictive formulas for lumbar lordosis (LL) using pelvic incidence (PI) for improved spinal balance assessment.

Main Methods:

• Cross-sectional study of 726 asymptomatic individuals.
• Radiographic assessment of sagittal parameters.
• K-means clustering for participant categorization.
• Decision tree analysis using PI and sacral slope (SS) for classification criteria.
• Linear regression models to predict LL and pelvic tilt (PT).

Main Results:

• Three distinct clusters of spinopelvic alignment were identified.
• Cluster-specific predictive formulas for LL and SS were generated.
• A moderate correlation was observed between PI and LL in one cluster (LL = 0.68*PI + 24.82).

Conclusions:

• Distinct patterns of sagittal balance were identified in the asymptomatic population.
• Cluster-specific predictive formulas for LL based on PI enhance prediction accuracy.
• Recognizing the anteverted pelvic subgroup is vital for precise LL and SS prediction in spinal surgery planning.