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Predicting Postoperative Outcomes In Lumbar Spinal Fusion: Development Of A Machine Learning Model London Spine Lumbar Stenosis

The article discusses the development of a machine-learning algorithm to predict surgical outcomes in patients with degenerative lumbar spondylolisthesis (DLS) undergoing spinal fusion surgery. The study conducted a retrospective analysis of patients who underwent spinal fusion surgery between January 2016 and December 2018. Four different machine learning models were trained and evaluated for their performance in predicting the occurrence of lower back pain after two years. The XGBoost model demonstrated the best performance with an AUC of 0.81. Age, composition of the erector spinae, and severity of lumbar spinal stenosis were identified as the most important features. The study highlights the potential of machine learning algorithms in predicting surgical outcomes and enhancing patient care by identifying high-risk individuals and modifiable risk factors

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
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Published article

CONCLUSIONS: This study represents a novel approach to predicting surgical outcomes in spinal fusion patients. The XGBoost demonstrated a better performance compared to classical models and highlighted the potential contributions of age and paraspinal musculature atrophy as significant factors. These findings have important implications for enhancing patient care through the identification of high-risk individuals and modifiable risk factors. As the incorporation of machine learning algorithms…

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Abstract Background context: Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to,

Abstract

Background context: Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to enhance the precision and comprehensiveness of preoperative evaluations.

Purpose: We aimed to develop a machine-learning algorithm to predict surgical outcomes in patients with degenerative lumbar spondylolisthesis (DLS) undergoing spinal fusion surgery, only using preoperative data.

Study design: Retrospective cross-sectional study.

Patient sample: Patients with DLS undergoing lumbar spinal fusion surgery OUTCOME MEASURES: : This study aimed to predict the occurrence of lower back pain (LBP) ≥ 4 on the numeric analogue scale (NAS) two years after surgery. LBP was evaluated as the average pain patients experienced at rest in the week before questioning. NAS ranges from 0 to 10, 0 representing no pain and 10 representing the worst pain imaginable.

Methods: We conducted a retrospective analysis of prospectively enrolled patients who underwent spinal fusion surgery for degenerative lumbar spondylolistheses at our institution in the United States between January 2016 and December 2018. The initial patient characteristics to be included in the training of the model were chosen by clinical expertise and through a literature review and included demographic characteristics, comorbidities, and radiologic features. The data was split into a training and validation datasets using a 60/40 split. Four different machine learning models were trained, including the modern XGBoost model, logistic regression, random-forest, and support vector machine (SVM). The models were evaluated according to the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. An AUC of 0.7 to 0.8 was considered fair, 0.8 to 0.9 good, and ≥ 0.9 excellent. Additionally, a calibration plot and the Brier score were calculated for each model.

Results: A total of 135 patients (66% female) were included. 38 (28%) patients reported LBP ≥ 4 after two years, representing the positive class. The XGBoost model demonstrated the best performance in the validation set with an AUC of 0.81 (95% CI 0.67 – 0.95). The other machine learning models performed significantly worse: with an AUC of 0.52 (95% CI 0.37 – 0.68) for the SVM, 0.56 (95% CI 0.37 – 0.76) for the logistic regression and an AUC of 0.56 (95% CI 0.37 – 0.78) for the random forest. In the XGBoost model age, composition of the erector spinae, and severity of lumbar spinal stenosis as were identified as the most important features.

Conclusions: This study represents a novel approach to predicting surgical outcomes in spinal fusion patients. The XGBoost demonstrated a better performance compared to classical models and highlighted the potential contributions of age and paraspinal musculature atrophy as significant factors. These findings have important implications for enhancing patient care through the identification of high-risk individuals and modifiable risk factors. As the incorporation of machine learning algorithms into clinical decision-making continues to gain traction in research and clinical practice, our insights reinforce this trajectory by showcasing the potential of these techniques in forecasting surgical results.

Keywords: Degenerative Lumbar Spondylolisthesis; Machine Learning; Random Forest; Spinal Fusion; Support Vector Machine; Xtreme Gradient Boosting.

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Predicting Postoperative Outcomes in Lumbar Spinal Fusion: development of a machine learning model

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Abstract Background context: Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to

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