STUDY DESIGN: Retrospective, radiographical analysis of mathe-matical formulas used to predict sagittal vertical axis (SVA) after pedicle subtraction osteotomy (PSO). OBJECTIVE: Evaluate the ability of different formulas to predict SVA after PSO. SUMMARY OF BACKGROUND DATA: Failure to achieve optimal spinal alignment after spinal fusion correlates with poor outcomes. Numerous mathematical models have been proposed to aid preoperative PSO planning and predict postoperative SVA. Pelvic parameters have been shown to impact spinal alignment; however, many preoperative planning models fail to evaluate these. Compensatory changes within unfused spinal segments have also been shown to impact SVA. Predictive formulas that do not evaluate pelvic parameters and unfused spinal segments may erroneously guide PSO surgery. A formula that integrates pelvic tilt (PT) and spinal compensatory changes to predict optimal SVA has been previously proposed. METHODS: Comparative analysis of 5 mathematical models used to predict optimal postoperative SVA (<5 cm) after PSO was performed using a multicenter PSO database. RESULTS: Radiographs of 147 patients, mean age 52 years (SD = 15 yr), who received 147 PSOs (42 thoracic and 105 lumbar) were evaluated. Mean preoperative and postoperative SVA was 108 mm (SD = 95 mm) and 30 mm (SD = 60 mm; P < 0.001), respectively. Each mathematical formula provided unique prediction for postoperative SA (Pearson R < 0.15). Formulas that neglected pelvic alignment poorly predicted final SVA and poorly correlated with optimal SVA. Formulas that evaluated pelvic morphology (pelvic incidence) had improved SVA prediction. The Lafage formulas, which incorporate PT and spinal compensatory changes, had the best SVA prediction (P < 0.05) and best correlation with optimal SVA (R = 0.75). CONCLUSION: Preoperative planning for PSO is essential to optimize postoperative spinal alignment. Mathematical models that do not consider pelvic parameters and changes in unfused spinal segments poorly predict optimal postoperative alignment and may predispose to poor clinical outcomes. The Lafage formulas, which incorporated PT and spinal compensatory changes, best predicted optimal SVA.
STUDY DESIGN: Retrospective, radiographical analysis of mathe-matical formulas used to predict sagittal vertical axis (SVA) after pedicle subtraction osteotomy (PSO). OBJECTIVE: Evaluate the ability of different formulas to predict SVA after PSO. SUMMARY OF BACKGROUND DATA: Failure to achieve optimal spinal alignment after spinal fusion correlates with poor outcomes. Numerous mathematical models have been proposed to aid preoperative PSO planning and predict postoperative SVA. Pelvic parameters have been shown to impact spinal alignment; however, many preoperative planning models fail to evaluate these. Compensatory changes within unfused spinal segments have also been shown to impact SVA. Predictive formulas that do not evaluate pelvic parameters and unfused spinal segments may erroneously guide PSO surgery. A formula that integrates pelvic tilt (PT) and spinal compensatory changes to predict optimal SVA has been previously proposed. METHODS: Comparative analysis of 5 mathematical models used to predict optimal postoperative SVA (<5 cm) after PSO was performed using a multicenter PSO database. RESULTS: Radiographs of 147 patients, mean age 52 years (SD = 15 yr), who received 147 PSOs (42 thoracic and 105 lumbar) were evaluated. Mean preoperative and postoperative SVA was 108 mm (SD = 95 mm) and 30 mm (SD = 60 mm; P < 0.001), respectively. Each mathematical formula provided unique prediction for postoperative SA (Pearson R < 0.15). Formulas that neglected pelvic alignment poorly predicted final SVA and poorly correlated with optimal SVA. Formulas that evaluated pelvic morphology (pelvic incidence) had improved SVA prediction. The Lafage formulas, which incorporate PT and spinal compensatory changes, had the best SVA prediction (P < 0.05) and best correlation with optimal SVA (R = 0.75). CONCLUSION: Preoperative planning for PSO is essential to optimize postoperative spinal alignment. Mathematical models that do not consider pelvic parameters and changes in unfused spinal segments poorly predict optimal postoperative alignment and may predispose to poor clinical outcomes. The Lafage formulas, which incorporated PT and spinal compensatory changes, best predicted optimal SVA.