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.
Authors
- Erica F. Bisson (ORCID: https://orcid.org/0000-0002-8583-8544)
- Paul Park (ORCID: https://orcid.org/0000-0002-0375-396X)
- Steven D. Glassman (ORCID: https://orcid.org/0000-0002-5488-9357)
- Mohamad Bydon (ORCID: https://orcid.org/0000-0002-0543-396X)
- Christopher I. Shaffrey (ORCID: https://orcid.org/0000-0001-9760-8386)
- Cheerag D. Upadhyaya
- Oren N. Gottfried
- Andrew K. Chan (ORCID: https://orcid.org/0000-0002-9352-5281)
- Kevin T. Foley (ORCID: https://orcid.org/0000-0002-3111-3943)
- Domagoj Coric (ORCID: https://orcid.org/0000-0001-8521-2691)
- Luis Manuel Tumialan
- Michael S. Virk (ORCID: https://orcid.org/0000-0002-8511-5602)
- Leah Y. Carreon (ORCID: https://orcid.org/0000-0002-7685-9036)
- Dean Chou (ORCID: https://orcid.org/0000-0003-0310-8263)
- John J. Knightly (ORCID: https://orcid.org/0000-0003-0917-9265)
- Eric A. Potts (ORCID: https://orcid.org/0000-0003-4349-2327)
- Jay D. Turner
- Satvir Saggi (ORCID: https://orcid.org/0000-0002-4511-367X)
- Michael Y. Wang (ORCID: https://orcid.org/0000-0002-6997-5995)
- Anthony L. Asher (ORCID: https://orcid.org/0000-0002-3596-3849)
- Juan S. Uribe (ORCID: https://orcid.org/0000-0002-2150-4211)
- Praveen V. Mummaneni
- Kai-Ming Fu
- Regis W. Haid
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
- Field-Weighted Citation Impact
- 0.00