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
Heterotopic ossification (HO) is perhaps the
single most significant obstacle to independence, functional mobility, and
return to duty for combat-injured veterans of Operation Enduring
Freedom and Operation Iraqi Freedom. Recent research into the cause(s)
of HO has been driven by a markedly higher prevalence seen in these
wounded warriors than encountered in previous wars or following
civilian trauma. To that end, research in both civilian and military
laboratories continues to shed light onto the complex mechanisms
behind HO formation, including systemic and wound specific factors,
cell lineage, and neurogenic inflammation. Of particular interest,
non-invasive