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Bone & Joint Open
Vol. 4, Issue 11 | Pages 873 - 880
17 Nov 2023
Swaby L Perry DC Walker K Hind D Mills A Jayasuriya R Totton N Desoysa L Chatters R Young B Sherratt F Latimer N Keetharuth A Kenison L Walters S Gardner A Ahuja S Campbell L Greenwood S Cole A

Aims

Scoliosis is a lateral curvature of the spine with associated rotation, often causing distress due to appearance. For some curves, there is good evidence to support the use of a spinal brace, worn for 20 to 24 hours a day to minimize the curve, making it as straight as possible during growth, preventing progression. Compliance can be poor due to appearance and comfort. A night-time brace, worn for eight to 12 hours, can achieve higher levels of curve correction while patients are supine, and could be preferable for patients, but evidence of efficacy is limited. This is the protocol for a randomized controlled trial of ‘full-time bracing’ versus ‘night-time bracing’ in adolescent idiopathic scoliosis (AIS).

Methods

UK paediatric spine clinics will recruit 780 participants aged ten to 15 years-old with AIS, Risser stage 0, 1, or 2, and curve size (Cobb angle) 20° to 40° with apex at or below T7. Patients are randomly allocated 1:1, to either full-time or night-time bracing. A qualitative sub-study will explore communication and experiences of families in terms of bracing and research. Patient and Public Involvement & Engagement informed study design and will assist with aspects of trial delivery and dissemination.


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.