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Bone & Joint Research
Vol. 12, Issue 4 | Pages 245 - 255
3 Apr 2023
Ryu S So J Ha Y Kuh S Chin D Kim K Cho Y Kim K

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.


Bone & Joint Research
Vol. 1, Issue 8 | Pages 174 - 179
1 Aug 2012
Alfieri KA Forsberg JA Potter BK

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 in vivo testing using Raman spectroscopy may become a feasible modality for early detection, and a wound-specific model designed to detect the early gene transcript signatures associated with HO is being tested. Through a combined effort, the goals of early detection, risk stratification, and development of novel systemic and local prophylaxis may soon be attainable.