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Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study – Lumbar Fusion

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This article discusses the development of a risk prediction model for cage subsidence following instrumented posterior lumbar fusion surgery. The study reviewed 59 patients and collected data on various factors that can contribute to subsidence. The data were analyzed using conventional statistical analysis methods, stepwise regression, and machine learning algorithms to build a predictive model. The study found significant differences in certain factors between the subsidence and non-subsidence groups. The authors developed a Cage Subsidence Score (CSS) based on these factors and evaluated its diagnostic efficiency. The machine learning algorithms showed promising results in predicting the risk of subsidence. Overall, the study confirms the potential of machine learning in solving practical clinical problems and shows the importance of an individualized and comprehensive evaluation of the risk of cage subsidence

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : best recognised spine facility in UK

Published article

CONCLUSION: The machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.

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Front Med (Lausanne). 2023 Jul 21;10:1196384. doi: 10.3389/fmed.2023.1196384. eCollection 2023.ABSTRACTBACKGROUND: Interbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an,

Front Med (Lausanne). 2023 Jul 21;10:1196384. doi: 10.3389/fmed.2023.1196384. eCollection 2023.

ABSTRACT

BACKGROUND: Interbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an individualized and comprehensive evaluation of the risk of cage subsidence following the surgery. So we attempt to identify potential risk factors and develop a risk prediction model that can predict the possibility of subsidence by providing a Cage Subsidence Score (CSS) after surgery, and evaluate whether machine learning-related techniques can effectively predict the subsidence.

METHODS: This study reviewed 59 patients who underwent posterior lumbar fusion in our hospital from 2014 to 2019. They were divided into a subsidence group and a non-subsidence group according to whether the interbody fusion cage subsidence occurred during follow-up. Data were collected on the patient, including age, sex, cage segment, number of fusion segments, preoperative space height, postoperative space height, preoperative L4 lordosis Angle, postoperative L4 lordosis Angle, preoperative L5 lordosis Angle, postoperative PT, postoperative SS, postoperative PI. The conventional statistical analysis method was used to find potential risk factors that can lead to subsidence, then the results were incorporated into stepwise regression and machine learning algorithms, respectively, to build a model that could predict the subsidence. Finally the diagnostic efficiency of prediction is verified.

RESULTS: Univariate analysis showed significant differences in pre-/postoperative intervertebral disc height, postoperative L4 segment lordosis, postoperative PT, and postoperative SS between the subsidence group and the non-subsidence group (p < 0.05). The CSS was trained by stepwise regression: 2 points for postoperative disc height > 14.68 mm, 3 points for postoperative L4 segment lordosis angle >16.91°, and 4 points for postoperative PT > 22.69°. If the total score is larger than 0.5, it is the high-risk subsidence group, while less than 0.5 is low-risk. The score obtains the area under the curve (AUC) of 0.857 and 0.806 in the development and validation set, respectively. The AUC of the GBM model based on the machine learning algorithm to predict the risk in the training set is 0.971 and the validation set is 0.889. The AUC of the avNNet model reached 0.931 in the training set and 0.868 in the validation set, respectively.

CONCLUSION: The machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.

PMID:37547617 | PMC:PMC10401589 | DOI:10.3389/fmed.2023.1196384

The London Spine Unit : best recognised spine facility in UK

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Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study

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Front Med (Lausanne). 2023 Jul 21;10:1196384. doi: 10.3389/fmed.2023.1196384. eCollection 2023.ABSTRACTBACKGROUND: Interbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an

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