STUDY DESIGN: Prospective and retrospective radiographic study of adult patients with spinal deformities. OBJECTIVE: Construct predictive models for pelvic tilt (PT) and global sagittal balance (sagittal vertical axis [SVA]) and evaluate the effectiveness of these predictive models against a group of patients after pedicle subtraction osteotomy. SUMMARY OF BACKGROUND DATA: Spinal balance involves a complex interaction between the pelvis and vertebral column. In the setting of adult spinal deformity, prediction of postoperative alignment can be challenging. METHODS: The study included 219 adult patients treated for spinal deformity. Full-length standing films were available for all subjects. Multilinear models with a stepwise condition were used on the first group of patients (n = 179) to predict PT and global sagittal balance (measured by the SVA). Prediction models were then applied on a second group of patients (n = 40) to estimate postoperative radiographic parameters after pedicle subtraction osteotomy surgery. Differences between estimated parameters and real values were evaluated. RESULTS: Multilinear regression analysis applied on the first group of patients led to a predictive formula for PT (r = 0.93, standard error = 4.4°) using the following parameters: pelvic incidence, maximal lordosis, and maximal kyphosis. These parameters with the addition of the predicted PT were then used to predict the SVA (r = 0.89, standard error = 32 mm). Validation of predictive models (second group of patients) used pelvic incidence and postoperative sagittal curves. Postoperative PT was predicted with a mean error of 4.3° (SD 3.5°) and postoperative SVA was predicted with a mean error of 29 mm (SD = 23 mm). CONCLUSION: This is the first study to develop and validate pragmatic predictive models for key spino-pelvic parameters (PT and SVA) in the setting of adult spinal deformity. Using a morphologic pelvic parameter (pelvic incidence) and spinal parameters modifiable through surgery (lumbar lordosis and thoracic kyphosis), postoperative sagittal alignment can be predicted.
STUDY DESIGN: Prospective and retrospective radiographic study of adult patients with spinal deformities. OBJECTIVE: Construct predictive models for pelvic tilt (PT) and global sagittal balance (sagittal vertical axis [SVA]) and evaluate the effectiveness of these predictive models against a group of patients after pedicle subtraction osteotomy. SUMMARY OF BACKGROUND DATA: Spinal balance involves a complex interaction between the pelvis and vertebral column. In the setting of adult spinal deformity, prediction of postoperative alignment can be challenging. METHODS: The study included 219 adult patients treated for spinal deformity. Full-length standing films were available for all subjects. Multilinear models with a stepwise condition were used on the first group of patients (n = 179) to predict PT and global sagittal balance (measured by the SVA). Prediction models were then applied on a second group of patients (n = 40) to estimate postoperative radiographic parameters after pedicle subtraction osteotomy surgery. Differences between estimated parameters and real values were evaluated. RESULTS: Multilinear regression analysis applied on the first group of patients led to a predictive formula for PT (r = 0.93, standard error = 4.4°) using the following parameters: pelvic incidence, maximal lordosis, and maximal kyphosis. These parameters with the addition of the predicted PT were then used to predict the SVA (r = 0.89, standard error = 32 mm). Validation of predictive models (second group of patients) used pelvic incidence and postoperative sagittal curves. Postoperative PT was predicted with a mean error of 4.3° (SD 3.5°) and postoperative SVA was predicted with a mean error of 29 mm (SD = 23 mm). CONCLUSION: This is the first study to develop and validate pragmatic predictive models for key spino-pelvic parameters (PT and SVA) in the setting of adult spinal deformity. Using a morphologic pelvic parameter (pelvic incidence) and spinal parameters modifiable through surgery (lumbar lordosis and thoracic kyphosis), postoperative sagittal alignment can be predicted.
Authors: Virginie Lafage; Justin S Smith; Shay Bess; Frank J Schwab; Christopher P Ames; Eric Klineberg; Vincent Arlet; Richard Hostin; Douglas C Burton; Christopher I Shaffrey Journal: Eur Spine J Date: 2011-08-12 Impact factor: 3.134
Authors: H Koller; C Ames; H Mehdian; R Bartels; R Ferch; V Deriven; H Toyone; C Shaffrey; J Smith; W Hitzl; J Schröder; Yohan Robinson Journal: Eur Spine J Date: 2018-11-27 Impact factor: 3.134