Early Detection of Progressive Adolescent Idiopathic Scoliosis: A Severity Index.
Lippincott, Williams & Wilkins
This is the author accepted manuscript. The final version is available from Lippincott, Williams & Wilkins via the DOI in this record.
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STUDY DESIGN: Early detection of progressive adolescent idiopathic scoliosis (AIS) was assessed based on 3D quantification of the deformity. OBJECTIVE: Based on 3D quantitative description of scoliosis curves, the aim is to assess a specific phenotype that could be an early detectable severity index for progressive AIS. SUMMARY OF BACKGROUND DATA: Early detection of progressive scoliosis is important for adapted treatment to limit progression. However, progression risk assessment is mainly based on the follow up, waiting for signs of rapid progression that generally occur during the growth peak. METHODS: 65 mild scoliosis (16 boys, 49 girls, Cobb Angle between 10 and 20°) with a Risser between 0 and 2 were followed from their first exam until a decision was made by the clinician, either considering the spine as stable at the end of growth (26 patients) or planning to brace because of progression (39 patients). Calibrated bi-planar X-rays were performed and 3D reconstructions of the spine allowed to calculate six local parameters related to main curve deformity. For progressive curve 3D phenotype assessment, data were compared to those previously assessed for 30 severe scoliosis (Cobb Angle > 35°), 17 scoliosis before brace (Cobb Angle > 29°) and 53 spines of non-scoliosis subjects. A predictive discriminant analysis was performed to assess similarity of mild scoliosis curves either to those of scoliosis or non-scoliosis spines, yielding a severity index (S-index). S-index value at first exam was compared to clinical outcome. RESULTS: At the first exam, 53 out of 65 predictions (82%) were in agreement with actual clinical outcome. 89% of the curves that were predicted as progressive proved accurate. CONCLUSION: Although still requiring large scale validation, results are promising for early detection of progressive curves.
Published online 24 October 2016
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