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Research Article
Yang Qu
Jilin University Second Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: This study aimed to predict the C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables by using support vector machine (SVM) method.
Methods: We conducted a retrospective investigation based on 184 consecutive patients with CM after PLF, and data was collected from March 2013 to December 2019. Clinical and imaging variables were obtained and imported into univariable and multivariable logistics regression analysis to identify risk factors for C5P. According to published reports and clinical experience, a series of variables was selected to develop an SVM machine learning model to predict C5P. The accuracy (ACC), area under the receiver operating characteristic curve (AUC) and confusion matrices were used to evaluate the performance of the prediction model.
Results: Among the total 184 consecutive patients, C5P occurred in 26 patients (14.13%). Multivariate analyses demonstrated the following 4 independent factors associated with C5P: electromyogram abnormal (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of foraminal stenosis C4-C5 (OR = 15.492). The SVM model achieved an area under receiver operating characteristic curve (AUC) of 0.923 and ACC of 0.918. Meanwhile, the confusion matrix shown the classification results of the discriminant analysis.
Conclusions: The designed SVM model presented a satisfied performance in predicting C5P from routinely available variables. However, future external validation is needed.
Keywords
C5 palsy, Cervical myelopathy, Posterior laminectomy and fusion, Risk factors, Outcomes, Support vector machine
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CURRENT STATUS: Under Review
Version 1
Posted 17 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 23 Apr, 2021
Reviewer #4 agreed
On 09 Apr, 2021
Reviews received
Received 07 Apr, 2021
Review #2 received
Received 07 Apr, 2021
Reviewer #3 agreed
On 07 Apr, 2021
Review #3 received
Received 07 Apr, 2021
Reviewer #2 agreed
On 06 Apr, 2021
Review #1 received
Received 29 Mar, 2021
Reviewer #1 agreed
On 23 Mar, 2021
Reviewers invited
Invitations sent on 10 Mar, 2021
First submitted
On 09 Mar, 2021
Editor assigned
On 09 Mar, 2021
Submission checks complete
On 09 Mar, 2021
Editor invited
On 09 Mar, 2021
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Subject Areas
Orthopedic Surgery
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A new preprint design is coming!
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This preprint is under consideration at Journal of Orthopaedic Surgery and Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Yang Qu
Jilin University Second Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 17 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 23 Apr, 2021
Reviewer #4 agreed
On 09 Apr, 2021
Reviews received
Received 07 Apr, 2021
Review #2 received
Received 07 Apr, 2021
Reviewer #3 agreed
On 07 Apr, 2021
Review #3 received
Received 07 Apr, 2021
Reviewer #2 agreed
On 06 Apr, 2021
Review #1 received
Received 29 Mar, 2021
Reviewer #1 agreed
On 23 Mar, 2021
Reviewers invited
Invitations sent on 10 Mar, 2021
First submitted
On 09 Mar, 2021
Editor assigned
On 09 Mar, 2021
Submission checks complete
On 09 Mar, 2021
Editor invited
On 09 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Orthopedic Surgery
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: This study aimed to predict the C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables by using support vector machine (SVM) method.
Methods: We conducted a retrospective investigation based on 184 consecutive patients with CM after PLF, and data was collected from March 2013 to December 2019. Clinical and imaging variables were obtained and imported into univariable and multivariable logistics regression analysis to identify risk factors for C5P. According to published reports and clinical experience, a series of variables was selected to develop an SVM machine learning model to predict C5P. The accuracy (ACC), area under the receiver operating characteristic curve (AUC) and confusion matrices were used to evaluate the performance of the prediction model.
Results: Among the total 184 consecutive patients, C5P occurred in 26 patients (14.13%). Multivariate analyses demonstrated the following 4 independent factors associated with C5P: electromyogram abnormal (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of foraminal stenosis C4-C5 (OR = 15.492). The SVM model achieved an area under receiver operating characteristic curve (AUC) of 0.923 and ACC of 0.918. Meanwhile, the confusion matrix shown the classification results of the discriminant analysis.
Conclusions: The designed SVM model presented a satisfied performance in predicting C5P from routinely available variables. However, future external validation is needed.
Figure 1
Figure 2
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