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Automated machine learning-based model for the prediction of pedicle screw loosening after degenerative lumbar fusion surgery – Lumbar Fusion

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The study aimed to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery using machine learning algorithms. A total of 552 patients were included, and the XGBoost model exhibited the best performance in predicting pedicle screw loosening, with an AUC of 0.884 in the test set. Ten crucial variables were identified, and the model demonstrated substantial accuracy in the prospective cohort. This study provides valuable information for clinical decision-making in achieving successful spinal fusion

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
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Published article

The adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key features associated with pedicle screw loosening. Patient clinical characteristics,…

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Biosci Trends. 2024 Feb 27. doi: 10.5582/bst.2023.01327. Online ahead of print.ABSTRACTThe adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular,

Biosci Trends. 2024 Feb 27. doi: 10.5582/bst.2023.01327. Online ahead of print.

ABSTRACT

The adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key features associated with pedicle screw loosening. Patient clinical characteristics, intraoperative variables, and radiographic parameters were collected and used to construct eight machine learning models, including a training set (80% of participants) and a test set (20% of participants). The XGBoost model exhibited the best performance, with an AUC of 0.884 (95% CI: 0.825-0.944) in the test set, along with the lowest Brier score. Ten crucial variables, including age, disease diagnosis: degenerative scoliosis, number of fused levels, fixation to S1, HU value, preoperative PT, preoperative PI-LL, postoperative LL, postoperative PT, and postoperative PI-LL were selected. In the prospective cohort, the XGBoost model demonstrated substantial performance with an accuracy of 83.32%. This study identified crucial variables associated with pedicle screw loosening after degenerative lumbar fusion surgery and successfully developed a machine learning model to predict pedicle screw loosening. The findings of this study may provide valuable information for clinical decision-making.

PMID:38417874 | DOI:10.5582/bst.2023.01327

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Automated machine learning-based model for the prediction of pedicle screw loosening after degenerative lumbar fusion surgery

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Biosci Trends. 2024 Feb 27. doi: 10.5582/bst.2023.01327. Online ahead of print.ABSTRACTThe adequacy of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variables and predict pedicle screw loosening after degenerative lumbar fusion surgery. A total of 552 patients who underwent primary transpedicular
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