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
Although vertebroplasty is very effective for relieving acute pain from an osteoporotic vertebral compression fracture, not all patients who undergo vertebroplasty receive the same degree of benefit from the procedure. In order to identify the ideal candidate for vertebroplasty, pre-operative prognostic demographic or clinico-radiological factors need to be identified. The objective of this study was to identify the pre-operative prognostic factors related to the effect of vertebroplasty on acute pain control using a cohort of surgically and non-surgically managed patients. Patients with single-level acute osteoporotic vertebral compression fracture at thoracolumbar junction (T10 to L2) were followed. If the patients were not satisfied with acute pain reduction after a three-week conservative treatment, vertebroplasty was recommended. Pain assessment was carried out at the time of diagnosis, as well as three, four, six, and 12 weeks after the diagnosis. The effect of vertebroplasty, compared with conservative treatment, on back pain (visual analogue score, VAS) was analysed with the use of analysis-of-covariance models that adjusted for pre-operative VAS scores.Objectives
Patients and Methods
