Aims. 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. Methods. 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. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of
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
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Loss of motion following spine segment fusion results in increased strain in the adjacent motion segments. However, to date, studies on the biomechanics of the cervical spine have not assessed the role of coupled motions in the lumbar spine. Accordingly, we investigated the biomechanics of the cervical spine following cervical fusion and lumbar fusion during simulated whiplash using a whole-human finite element (FE) model to simulate coupled motions of the spine. A previously validated FE model of the human body in the driver-occupant position was used to investigate cervical hyperextension injury. The cervical spine was subjected to simulated whiplash exposure in accordance with Euro NCAP (the European New Car Assessment Programme) testing using the whole human FE model. The coupled motions between the cervical spine and lumbar spine were assessed by evaluating the biomechanical effects of simulated cervical fusion and lumbar fusion.Objectives
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