2019 A Machine Learning Powered Calculator for Prediction of 2 Year Post-Operative Neck Pain in Patients With Cervical Spondylotic Myelopathy: A Quality Outcomes Database Study

INTRODUCTION: Neck pain is a common symptom in patients with cervical spondylotic myelopathy (CSM). Machine learning (ML) can be leveraged to develop prediction tools that can predict post-operative neck pain in patients with CSM. METHODS: We analyzed the prospective Quality Outcomes Database consisting of patients with CSM who underwent cervical spine surgery. 1141 patients were split into an 80% training/20% testing cohort. ML algorithms were evaluated on their performance to predict achievement of the minimum clinically important difference (MCID) in Visual Analogue Scale-Neck Pain (VAS-NP) at 2 years post-operatively given a set of 20 pre-operative features. Hyperparameter tuning was performed with 5-fold cross-validation. Recursive feature selection was used to select 5 key variables for predicting VAS-NP MCID achievement. Model performance was assessed with accuracy, area under the receiver operating curve (AUROC), precision, recall/sensitivity, and specificity. The final model was evaluated on the testing cohort. A GUI was created using the Shiny application software to predict post-operative neck pain. RESULTS: Random forest models demonstrated the best performance for predicting 2 year post-operative neck pain. Random forest model achieved 80% cross-validation accuracy for predicting MCID in neck pain. Recursive feature selection identified age, baseline VAS-NP, baseline modified Japanese Orthopedic Association (mJOA) score, baseline EQ-VAS score, and BMI as the most important variables for predicting post-operative neck pain. Final models using these variables achieved 80% accuracy and AUROC during the testing phase. Finally, we developed the first web interface to allow prediction of post-operative neck pain with user-inputted data. CONCLUSIONS: Baseline scores for VAS-NP, mJOA, EQ-VAS, BMI, and age are important features for predicting post-operative neck pain. ML calculators using these variables can be used in the clinical setting to predict post-operative neck pain with good accuracy.

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Publication Details

Journal
Neurosurgery
Published
2025-03-14
DOI
https://doi.org/10.1227/neu.0000000000003360_2019
Primary Topic
Medical Imaging and Analysis
Type
article
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article

2019 A Machine Learning Powered Calculator for Prediction of 2 Year Post-Operative Neck Pain in Patients With Cervical Spondylotic Myelopathy: A Quality Outcomes Database Study

Erica F. Bisson, Paul Park, Steven D. Glassman, Mohamad Bydon et al.
Neurosurgery
Medical Imaging and Analysis
article

2019 A Machine Learning Powered Calculator for Prediction of 2 Year Post-Operative Neck Pain in Patients With Cervical Spondylotic Myelopathy: A Quality Outcomes Database Study

Erica F. Bisson, Paul Park, Steven D. Glassman, Mohamad Bydon, Christopher I. Shaffrey, Cheerag D. Upadhyaya, Oren N. Gottfried, Andrew K. Chan, Kevin T. Foley, Domagoj Coric, Luis Manuel Tumialan, Michael S. Virk, Leah Y. Carreon, Dean Chou, John J. Knightly, Eric A. Potts, Jay D. Turner, Satvir Saggi, Michael Y. Wang, Anthony L. Asher, Juan S. Uribe, Praveen V. Mummaneni, Kai-Ming Fu, Regis W. Haid
article en

Abstract

INTRODUCTION: Neck pain is a common symptom in patients with cervical spondylotic myelopathy (CSM). Machine learning (ML) can be leveraged to develop prediction tools that can predict post-operative neck pain in patients with CSM. METHODS: We analyzed the prospective Quality Outcomes Database consisting of patients with CSM who underwent cervical spine surgery. 1141 patients were split into an 80% training/20% testing cohort. ML algorithms were evaluated on their performance to predict achievement of the minimum clinically important difference (MCID) in Visual Analogue Scale-Neck Pain (VAS-NP) at 2 years post-operatively given a set of 20 pre-operative features. Hyperparameter tuning was performed with 5-fold cross-validation. Recursive feature selection was used to select 5 key variables for predicting VAS-NP MCID achievement. Model performance was assessed with accuracy, area under the receiver operating curve (AUROC), precision, recall/sensitivity, and specificity. The final model was evaluated on the testing cohort. A GUI was created using the Shiny application software to predict post-operative neck pain. RESULTS: Random forest models demonstrated the best performance for predicting 2 year post-operative neck pain. Random forest model achieved 80% cross-validation accuracy for predicting MCID in neck pain. Recursive feature selection identified age, baseline VAS-NP, baseline modified Japanese Orthopedic Association (mJOA) score, baseline EQ-VAS score, and BMI as the most important variables for predicting post-operative neck pain. Final models using these variables achieved 80% accuracy and AUROC during the testing phase. Finally, we developed the first web interface to allow prediction of post-operative neck pain with user-inputted data. CONCLUSIONS: Baseline scores for VAS-NP, mJOA, EQ-VAS, BMI, and age are important features for predicting post-operative neck pain. ML calculators using these variables can be used in the clinical setting to predict post-operative neck pain with good accuracy.

NeurosurgeryVol. 71(Supplement_1)
Openalex Percentile: Top 96%
Medical Imaging and Analysis
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