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
To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism.Aims
Methods
The revised Tokuhashi, Tomita and modified Bauer
scores are commonly used to make difficult decisions in the management
of patients presenting with spinal metastases. A prospective cohort
study of 199 consecutive patients presenting with spinal metastases,
treated with either surgery and/or radiotherapy, was used to compare
the three systems. Cox regression, Nagelkerke’s R2 and
Harrell’s concordance were used to compare the systems and find their
best predictive items. The three systems were equally good in terms
of overall prognostic performance. Their most predictive items were
used to develop the Oswestry Spinal Risk Index (OSRI), which has
a similar concordance, but a larger coefficient of determination
than any of these three scores. A bootstrap procedure was used to
internally validate this score and determine its prediction optimism. The OSRI is a simple summation of two elements: primary tumour
pathology (PTP) and general condition (GC): OSRI = PTP + (2 – GC). This simple score can predict life expectancy accurately in patients
presenting with spinal metastases. It will be helpful in making
difficult clinical decisions without the delay of extensive investigations. Cite this article:
