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
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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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