STUDY DESIGN: Reliability study of the computer-assisted SDSG (Spinal Deformity Study Group) classification of lumbosacral spondylolisthesis. OBJECTIVE: To assess the intra- and interobserver reliability of the computer-assisted SDSG classification of lumbosacral spondylolisthesis. SUMMARY OF BACKGROUND DATA: The SDSG has proposed a new classification of lumbosacral spondylolisthesis based on slip grade, pelvic incidence (PI), and sacro-pelvic and spinal balance. Three types of low-grade spondylolisthesis are described: low PI (type 1), normal PI (type 2), and high PI (type 3). High-grade spondylolisthesis are defined as type 4 (balanced sacro-pelvis), type 5 (retroverted sacro-pelvis with balanced spine), and type 6 (retroverted sacro-pelvis with unbalanced spine). METHODS: Full-length standing lateral radiographs of the spine of 40 subjects with lumbosacral spondylolisthesis were reviewed twice by 7 observers. Custom software was used by the observers to identify 7 anatomical landmarks on each radiograph to determine the SDSG type for all subjects. Percentage of agreement and κ coefficients were used to determine the intra- and interobserver reliability. RESULTS: All 6 types of spondylolisthesis described in the computer-assisted SDSG classification were identified. Overall intra- and interobserver agreements were 80% (κ: 0.74) and 71% (κ: 0.65), respectively. The intra- and interobserver agreements associated with computerized determination of slip grade were 92% (κ: 0.83) and 88% (κ: 0.78), respectively. As for computerized determination of sacro-pelvic and spinal balance, intra- and interobserver agreements were 86% (κ: 0.76) and 75% (κ: 0.63) for low-grade slips, whereas they were 88% (κ: 0.80) and 83% (κ: 0.75) for high-grade slips. CONCLUSION: Substantial intra- and interobserver reliability was found for the computer-assisted SDSG classification, and all 6 types of lumbosacral spondylolisthesis were identified. Refinement of the computer-assisted classification technique is, however, needed to further increase the reliability of the SDSG classification and facilitate its clinical use.
STUDY DESIGN: Reliability study of the computer-assisted SDSG (Spinal Deformity Study Group) classification of lumbosacral spondylolisthesis. OBJECTIVE: To assess the intra- and interobserver reliability of the computer-assisted SDSG classification of lumbosacral spondylolisthesis. SUMMARY OF BACKGROUND DATA: The SDSG has proposed a new classification of lumbosacral spondylolisthesis based on slip grade, pelvic incidence (PI), and sacro-pelvic and spinal balance. Three types of low-grade spondylolisthesis are described: low PI (type 1), normal PI (type 2), and high PI (type 3). High-grade spondylolisthesis are defined as type 4 (balanced sacro-pelvis), type 5 (retroverted sacro-pelvis with balanced spine), and type 6 (retroverted sacro-pelvis with unbalanced spine). METHODS: Full-length standing lateral radiographs of the spine of 40 subjects with lumbosacral spondylolisthesis were reviewed twice by 7 observers. Custom software was used by the observers to identify 7 anatomical landmarks on each radiograph to determine the SDSG type for all subjects. Percentage of agreement and κ coefficients were used to determine the intra- and interobserver reliability. RESULTS: All 6 types of spondylolisthesis described in the computer-assisted SDSG classification were identified. Overall intra- and interobserver agreements were 80% (κ: 0.74) and 71% (κ: 0.65), respectively. The intra- and interobserver agreements associated with computerized determination of slip grade were 92% (κ: 0.83) and 88% (κ: 0.78), respectively. As for computerized determination of sacro-pelvic and spinal balance, intra- and interobserver agreements were 86% (κ: 0.76) and 75% (κ: 0.63) for low-grade slips, whereas they were 88% (κ: 0.80) and 83% (κ: 0.75) for high-grade slips. CONCLUSION: Substantial intra- and interobserver reliability was found for the computer-assisted SDSG classification, and all 6 types of lumbosacral spondylolisthesis were identified. Refinement of the computer-assisted classification technique is, however, needed to further increase the reliability of the SDSG classification and facilitate its clinical use.
Authors: David McKean; James Allman Sutcliffe; Hassan El Hassan; Nassim Parvizi; Anuj Wali; Dinuke Warakaulle; James Teh; Edward Seel; Stuart Blagg; Richard J Hughes Journal: BJR Case Rep Date: 2017-08-04
Authors: Atticus Coscia; Katie Paige; Michael Hostetter; Kevin O'Neill; Matthew Coscia; Erin Coscia; Michael Coscia Journal: Global Spine J Date: 2020-09-16