Clinical Validation of a Novel Musculoskeletal Modeling Framework to Predict Postoperative Sagittal Alignment

A retrospective radiographic and biomechanical analysis of 108 thoracolumbar fusion patients from two clinical centers. OBJECTIVE. This study aimed to determine the validity of a computational framework for predicting postoperative patient posture based on preoperative imaging and surgical data in a large clinical sample. SUMMARY OF BACKGROUND DATA. Short-term and long-term studies on thoracolumbar fusion patients have discussed that a preoperative predictive model would benefit surgical planning and improve patient outcomes. Clinical studies have shown that postoperative alignment changes at the pelvis and intact spine levels may negatively affect postural balance and quality of life. However, it remains challenging to predict such changes preoperatively because of confounding surgical and patient factors. MATERIALS AND METHODS. Patient-specific musculoskeletal models incorporated weight, height, body mass index, age, pathology-associated muscle strength, preoperative sagittal alignment, and surgical treatment details. The sagittal alignment parameters predicted by the simulations were compared with those observed radiographically at a minimum of three months after surgery. RESULTS. Pearson correlation coefficients ranged from r=0.86 to 0.95, and mean errors ranged from 4.1° to 5.6°. The predictive accuracies for postoperative spinopelvic malalignment (pelvic incidence minus lumbar lordosis>10°) and sagittal imbalance parameters (TPA>14°, T9PA>7.4°, or LPA>7.2°) were between 81% and 94%. Patients treated with long fusion (greater than five segments) had relatively lower prediction errors for lumbar lordosis and spinopelvic mismatch than those in the local and short groups. CONCLUSIONS. The overall model performance with long constructs was superior to those of the local (one to two segments) and short (three to four segments) fusion cases. The clinical framework is a promising tool in development to enhance clinical judgment and to help design treatment strategies for predictable surgical outcomes. LEVEL OF EVIDENCE. 3