European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
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Somatosensory evoked potentials (SSEPs) are effective in detecting upper extremity positional injuries; however, causal factors for which patient population is most at risk are not well established. ⋯ Sex, patient positioning, length of procedure, and BMI are determinants for upper extremity neural compromise during thoracolumbar and lumbosacral spine surgeries.
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The human standing position requires permanent reciprocal spino-pelvic adjustments to obtain a dynamic and economic posture. This study focuses on a hypokyphotic Lenke 1 adolescent idiopathic scoliosis (AIS) patients cohort and points out their particular lumbo-pelvic adaptive mechanisms to maintain a neutral sagittal balance. ⋯ Hypokyphotic Lenke 1 AIS patients use lumbo-pelvic compensatory mechanisms to maintain their global balance with a poor effectiveness. Subjects with a low PI have a restricted range of LL adaptation. Attention should be paid during surgical planning not to overcorrect lordosis in the instrumented levels in case of non-selective fusion, that may induce posterior shift of the fusion mass and expose to junctional syndromes and poor functional outcomes in this particular patients.
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The classification of three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. Recent studies have investigated pattern classification based on explicit clinical parameters. An emerging trend however seeks to simplify complex spine geometries and capture the predominant modes of variability of the deformation. The objective of this study is to perform a 3D characterization and morphology analysis of the thoracic and thoraco/lumbar scoliotic spines (cross-sectional study). The presence of subgroups within all Lenke types will be investigated by analyzing a simplified representation of the geometric 3D reconstruction of a patient's spine, and to establish the basis for a new classification approach based on a machine learning algorithm. ⋯ The stacked auto-encoder analysis technique helped to simplify the complex nature of 3D spine models, while preserving the intrinsic properties that are typically measured with explicit parameters derived from the 3D reconstruction.