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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. 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


Bone & Joint Research
Vol. 12, Issue 6 | Pages 387 - 396
26 Jun 2023
Xu J Si H Zeng Y Wu Y Zhang S Shen B

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.

Methods

We conducted a transcriptome-wide association study (TWAS) of spinal canal stenosis by integrating genome-wide association study summary statistics (including 661 cases and 178,065 controls) derived from Biobank Japan, and pre-computed gene expression weights of skeletal muscle and whole blood implemented in FUSION software. To verify the TWAS results, the candidate genes were furthered compared with messenger RNA (mRNA) expression profiles of LSS to screen for common genes. Finally, Metascape software was used to perform enrichment analysis of the candidate genes and common genes.


Bone & Joint Research
Vol. 12, Issue 3 | Pages 202 - 211
7 Mar 2023
Bai Z Shou Z Hu K Yu J Meng H Chen C

Aims

This study was performed to explore the effect of melatonin on pyroptosis in nucleus pulposus cells (NPCs) and the underlying mechanism of that effect.

Methods

This experiment included three patients diagnosed with lumbar disc herniation who failed conservative treatment. Nucleus pulposus tissue was isolated from these patients when they underwent surgical intervention, and primary NPCs were isolated and cultured. Western blotting, reverse transcription polymerase chain reaction, fluorescence staining, and other methods were used to detect changes in related signalling pathways and the ability of cells to resist pyroptosis.


Bone & Joint Research
Vol. 9, Issue 10 | Pages 653 - 666
7 Oct 2020
Li W Li G Chen W Cong L

Aims

The aim of this study was to systematically compare the safety and accuracy of robot-assisted (RA) technique with conventional freehand with/without fluoroscopy-assisted (CT) pedicle screw insertion for spine disease.

Methods

A systematic search was performed on PubMed, EMBASE, the Cochrane Library, MEDLINE, China National Knowledge Infrastructure (CNKI), and WANFANG for randomized controlled trials (RCTs) that investigated the safety and accuracy of RA compared with conventional freehand with/without fluoroscopy-assisted pedicle screw insertion for spine disease from 2012 to 2019. This meta-analysis used Mantel-Haenszel or inverse variance method with mixed-effects model for heterogeneity, calculating the odds ratio (OR), mean difference (MD), standardized mean difference (SMD), and 95% confidence intervals (CIs). The results of heterogeneity, subgroup analysis, and risk of bias were analyzed.