Wafa Skalli1, Claudio Vergari, Eric Ebermeyer, Isabelle Courtois, Xavier Drevelle, Remi Kohler, Kariman Abelin-Genevois, Jean Dubousset. 1. *Arts et Metiers ParisTech, Institut de Biomecanique Humaine Georges Charpak, Paris, France †School of Physics and Astronomy, University of Exeter, Exeter, United Kingdom ‡Unite Rachis, CHU - Hopital Bellevue, Saint-Etienne, France §Department of Pediatric Orthopedics, Hospices Civils de Lyon, Claude Bernard Lyon 1 University, Lyon, France.
Abstract
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: Sixty-five mild scoliosis (16 boys, 49 girls, Cobb Angle between 10 and 20°) with a Risser between 0 and 2 were followed from their first examination 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 biplanar x-rays were performed and 3D reconstructions of the spine allowed calculating six local parameters related to main curve deformity. For progressive curve 3D phenotype assessment, data were compared with those previously assessed for 30 severe scoliosis (Cobb Angle > 35°), 17 scoliosis before brace (Cobb Angle > 29°) and 53 spines of nonscoliosis subjects. A predictive discriminant analysis was performed to assess similarity of mild scoliosis curves either to those of scoliosis or nonscoliosis spines, yielding a severity index (S-index). S-index value at first examination was compared with clinical outcome. RESULTS: At the first exam, 53 out of 65 predictions (82%) were in agreement with actual clinical outcome. Approximately, 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. LEVEL OF EVIDENCE: 2.
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: Sixty-five mild scoliosis (16 boys, 49 girls, Cobb Angle between 10 and 20°) with a Risser between 0 and 2 were followed from their first examination 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 biplanar x-rays were performed and 3D reconstructions of the spine allowed calculating six local parameters related to main curve deformity. For progressive curve 3D phenotype assessment, data were compared with those previously assessed for 30 severe scoliosis (Cobb Angle > 35°), 17 scoliosis before brace (Cobb Angle > 29°) and 53 spines of nonscoliosis subjects. A predictive discriminant analysis was performed to assess similarity of mild scoliosis curves either to those of scoliosis or nonscoliosis spines, yielding a severity index (S-index). S-index value at first examination was compared with clinical outcome. RESULTS: At the first exam, 53 out of 65 predictions (82%) were in agreement with actual clinical outcome. Approximately, 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. LEVEL OF EVIDENCE: 2.
Authors: Lori A Dolan; Stuart L Weinstein; Mark F Abel; Patrick P Bosch; Matthew B Dobbs; Tyler O Farber; Matthew F Halsey; M Timothy Hresko; Walter F Krengel; Charles T Mehlman; James O Sanders; Richard M Schwend; Suken A Shah; Kushagra Verma Journal: Spine Deform Date: 2019-11
Authors: Zongshan Hu; Claudio Vergari; Laurent Gajny; Zhen Liu; Tsz-Ping Lam; Zezhang Zhu; Yong Qiu; Gene C W Man; Kwong-Hang Yeung; Winnie C W Chu; Jack C Y Cheng; Wafa Skalli Journal: Quant Imaging Med Surg Date: 2021-07
Authors: Tristan Langlais; Pierre Desprairies; Raphael Pietton; Pierre-Yves Rohan; Jean Dubousset; Judith R Meakin; Peter C Winlove; Raphael Vialle; Wafa Skalli; Claudio Vergari Journal: Biomech Model Mechanobiol Date: 2019-06-20