To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology.Aims
Methods
The objective of this study was to assess the association between whole body sagittal balance and risk of falls in elderly patients who have sought treatment for back pain. Balanced spinal sagittal alignment is known to be important for the prevention of falls. However, spinal sagittal imbalance can be markedly compensated by the lower extremities, and whole body sagittal balance including the lower extremities should be assessed to evaluate actual imbalances related to falls. Patients over 70 years old who visited an outpatient clinic for back pain treatment and underwent a standing whole-body radiograph were enrolled. Falls were prospectively assessed for 12 months using a monthly fall diary, and patients were divided into fallers and non-fallers according to the history of falls. Radiological parameters from whole-body radiographs and clinical data were compared between the two groups.Objectives
Methods
