Prediction of clinical height gain from surgical posterior correction of idiopathic scoliosis.

Objective: The best predictors of height gain due to surgical correction are the number of fused vertebrae and the degrees of the corrected Cobb angle. Existing studies of predictive models measured the radiographic spinal height and did not report the clinical height gain. The aims of this study were to determine the best predictive factors of clinical height gain before surgical correction, construct a predictive model using patient population data for machine learning, and test the performance of this model on a validation population.

Methods: The authors reviewed 145 medical records of consecutive patients who underwent surgery that included placement of posterior spinal instrumentation and fusion for idiopathic scoliosis between 2012 and 2016. Standing and sitting clinical heights were measured before and after surgery in patients who had been surgically treated under similar conditions. Multivariate analysis was then performed and the results were used to develop a predictive model for height gain after surgery. The data from the included patients were randomly assigned to a learning set or a test set.

Results: In total, 116 patients were included in the analysis, for whom the average postoperative clinical height gain in a standing position was 4.2 ± 1.8 cm (range 0-11 cm). The best prediction model was calculated as follows: standing clinical height gain (cm) = 1 - 0.023 × sitting clinical height (cm) - 0.19 × Risser stage + 0.058 × Cobb preoperative angle (°) + 0.021 × T5-12 kyphosis (°) + 0.14 × number of levels fused. In the validation cohort, 91% of the predicted values had an error of less than one-half of the actual height gain.

Conclusions: This predictive model formula for calculating the potential postoperative height gain after surgical treatment can be used preoperatively to inform idiopathic scoliosis patients of what outcomes they may expect from posterior spinal instrumentation and fusion (taking into account the model's uncertainty).