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The Bone & Joint Journal
Vol. 107-B, Issue 3 | Pages 337 - 345
1 Mar 2025
Wang D Wang Q Cui P Wang S Han D Chen X Lu S

Aims

Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desirable, with frequent complications and limited improvement in quality of life. The accurate prediction of surgical outcome is crucial to the process of clinical decision-making. Consequently, the aim of this study was to develop and validate a model for predicting an ideal surgical outcome (ISO) two years after ASD surgery.

Methods

We conducted a retrospective analysis of 458 consecutive patients who had undergone spinal fusion surgery for ASD between January 2016 and June 2022. The outcome of interest was achievement of the ISO, defined as an improvement in patient-reported outcomes exceeding the minimal clinically important difference, with no postoperative complications. Three machine-learning (ML) algorithms – LASSO, RFE, and Boruta – were used to identify key variables from the collected data. The dataset was randomly split into training (60%) and test (40%) sets. Five different ML models were trained, including logistic regression, random forest, XGBoost, LightGBM, and multilayer perceptron. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC).


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 Open
Vol. 3, Issue 2 | Pages 123 - 129
1 Feb 2022
Bernard J Bishop T Herzog J Haleem S Lupu C Ajayi B Lui DF

Aims

Vertebral body tethering (VBT) is a non-fusion technique to correct scoliosis. It allows correction of scoliosis through growth modulation (GM) by tethering the convex side to allow concave unrestricted growth similar to the hemiepiphysiodesis concept. The other modality is anterior scoliosis correction (ASC) where the tether is able to perform most of the correction immediately where limited growth is expected.

Methods

We conducted a retrospective analysis of clinical and radiological data of 20 patients aged between 9 and 17 years old, (with a 19 female: 1 male ratio) between January 2014 to December 2016 with a mean five-year follow-up (4 to 7).