Role of Neck Pain in Defining Clinical Trajectories of Outcomes in Patients With Degenerative Cervical Myelopathy: Results of a Novel Machine Learning Algorithm

Study DesignRetrospective analysis of prospective data.ObjectivesNeck pain represents a crucial factor underscoring a patient's decision to receive surgical intervention for degenerative cervical myelopathy (DCM). However, postoperative pain trajectories are poorly defined. This study aimed to employ machine learning-based trajectory modeling to identify patient subpopulations with distinct pain trajectories after surgery.MethodsWe pooled subjects from three major clinical studies on DCM. Group-based multivariate trajectory (GBMT) modeling was used to classify patients into distinct trajectories based on their neck pain score over one year. Outcome differences were examined with univariate analyses. Predictors of group membership were revealed with multinomial logistic regression.ResultsThree distinct trajectories of neck pain were identified from a total of 968 patients with DCM: "slow pain improvement" (n = 239; 25%), "no pain improvement" (n = 537; 55%), and "fast pain improvement" (n = 192; 20%) groups. Each trajectory exhibited a unique baseline pain profile. The "fast pain improvement" group, comprised of patients experiencing profound neck pain, had the best overall outcomes for pain, NDI, SF-36 PCS, and SF-36 MSC postoperatively. On the other hand, the "no pain improvement" group, consisting of patients with pain and multimodal impairment of moderate severity, had residual pain that remained constant and was least likely to experience functional outcome and quality of life improvement after one year.ConclusionsUnsupervised learning on neck pain identified unique pain recovery trajectories that consist of distinct patient phenotypes. Trajectory grouping offers an important framework to both identify novel DCM subpopulations and predict patterns of pain over time.Clinical Trials Included(1) Assessment of Surgical Techniques for Treating Cervical Spondylotic Myelopathy (CSM); https://clinicaltrials.gov/study/NCT00285337; ClinicalTrials.gov ID NCT00285337. (2) Surgical Treatment of Cervical Spondylotic Myelopathy; https://clinicaltrials.gov/study/NCT00565734; ClinicalTrals.gov ID NCT00565734. (3) Efficacy of Riluzole in Surgical Treatment for Cervical Spondylotic Myelopathy (CSM-Protect) (CSM-Protect); https://clinicaltrials.gov/study/NCT01257828; ClinicalTrials.gov ID NCT01257828.

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Publication Details

Journal
Global Spine Journal
Published
2025-05-10
DOI
https://doi.org/10.1177/21925682251341263
Citations
1
Primary Topic
Cervical and Thoracic Myelopathy
Type
article
Field-Weighted Citation Impact
2.01
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article

Role of Neck Pain in Defining Clinical Trajectories of Outcomes in Patients With Degenerative Cervical Myelopathy: Results of a Novel Machine Learning Algorithm

Michael George Fehlings, Mohammed Ali Alvi, Raymond Wong, Ayesha Quddusi
1 citations
Global Spine Journal
Cervical and Thoracic Myelopathy
2.01
article

Role of Neck Pain in Defining Clinical Trajectories of Outcomes in Patients With Degenerative Cervical Myelopathy: Results of a Novel Machine Learning Algorithm

Michael George Fehlings, Mohammed Ali Alvi, Raymond Wong, Ayesha Quddusi
article en
1 citations

Abstract

Study DesignRetrospective analysis of prospective data.ObjectivesNeck pain represents a crucial factor underscoring a patient's decision to receive surgical intervention for degenerative cervical myelopathy (DCM). However, postoperative pain trajectories are poorly defined. This study aimed to employ machine learning-based trajectory modeling to identify patient subpopulations with distinct pain trajectories after surgery.MethodsWe pooled subjects from three major clinical studies on DCM. Group-based multivariate trajectory (GBMT) modeling was used to classify patients into distinct trajectories based on their neck pain score over one year. Outcome differences were examined with univariate analyses. Predictors of group membership were revealed with multinomial logistic regression.ResultsThree distinct trajectories of neck pain were identified from a total of 968 patients with DCM: "slow pain improvement" (n = 239; 25%), "no pain improvement" (n = 537; 55%), and "fast pain improvement" (n = 192; 20%) groups. Each trajectory exhibited a unique baseline pain profile. The "fast pain improvement" group, comprised of patients experiencing profound neck pain, had the best overall outcomes for pain, NDI, SF-36 PCS, and SF-36 MSC postoperatively. On the other hand, the "no pain improvement" group, consisting of patients with pain and multimodal impairment of moderate severity, had residual pain that remained constant and was least likely to experience functional outcome and quality of life improvement after one year.ConclusionsUnsupervised learning on neck pain identified unique pain recovery trajectories that consist of distinct patient phenotypes. Trajectory grouping offers an important framework to both identify novel DCM subpopulations and predict patterns of pain over time.Clinical Trials Included(1) Assessment of Surgical Techniques for Treating Cervical Spondylotic Myelopathy (CSM); https://clinicaltrials.gov/study/NCT00285337; ClinicalTrials.gov ID NCT00285337. (2) Surgical Treatment of Cervical Spondylotic Myelopathy; https://clinicaltrials.gov/study/NCT00565734; ClinicalTrals.gov ID NCT00565734. (3) Efficacy of Riluzole in Surgical Treatment for Cervical Spondylotic Myelopathy (CSM-Protect) (CSM-Protect); https://clinicaltrials.gov/study/NCT01257828; ClinicalTrials.gov ID NCT01257828.

Global Spine JournalVol. 16(1)
University Health Network (CA), University of Toronto (CA), Health Net (US), Ontario Brain Institute (CA)
Openalex Percentile: Top 16%
Cervical and Thoracic Myelopathy
2.01
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