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. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

Aims

Lumbar spinal stenosis (LSS) is a common skeletal system disease that has been partly attributed to genetic variation. However, the correlation between genetic variation and pathological changes in LSS is insufficient, and it is difficult to provide a reference for the early diagnosis and treatment of the disease.

Aims

Anchorage of pedicle screw rod instrumentation in the elderly spine with poor bone quality remains challenging. Our study aims to evaluate how the screw bone anchorage is affected by screw design, bone quality, loading conditions, and cementing techniques.

Aims

Non-coding microRNA (miRNA) in extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) may promote neuronal repair after spinal cord injury (SCI). In this paper we report on the effects of MSC-EV-microRNA-381 (miR-381) in a rodent model of SCI.

Objectives

Loss of motion following spine segment fusion results in increased strain in the adjacent motion segments. However, to date, studies on the biomechanics of the cervical spine have not assessed the role of coupled motions in the lumbar spine. Accordingly, we investigated the biomechanics of the cervical spine following cervical fusion and lumbar fusion during simulated whiplash using a whole-human finite element (FE) model to simulate coupled motions of the spine.

Objectives

Cement augmentation of pedicle screws could be used to improve screw stability, especially in osteoporotic vertebrae. However, little is known concerning the influence of different screw types and amount of cement applied. Therefore, the aim of this biomechanical in vitro study was to evaluate the effect of cement augmentation on the screw pull-out force in osteoporotic vertebrae, comparing different pedicle screws (solid and fenestrated) and cement volumes (0 mL, 1 mL or 3 mL).