Prediction of the risk of C5 palsy after posterior laminectomy and fusion with cervical myelopathy using a support vector machine: an analysis of 184 consecutive patients

• DOI: 10.1186/s13018-021-02476-5
• Publication Date: 2021-05-21T06:02:25Z
• Authors: Haosheng Wang; Zhi-Ri Tang; Wenle Li; Tingting Fan; Jianwu Zhao; Mingyang Kang; Rongpeng Dong; Yang Qu
• Keywords: Male; C5 palsy; Support vector machine; Support Vector Machine; Outcomes; Diseases of the musculoskeletal system; Spinal Cord Diseases; 03 medical and health sciences; Postoperative Complications; 0302 clinical medicine; Risk Factors; Humans; Paralysis; Aged; Retrospective Studies; Orthopedic surgery; Laminectomy; Cervical myelopathy; Middle Aged; 3. Good health; Logistic Models; Spinal Fusion; Risk factors; RC925-935; ROC Curve; Multivariate Analysis; Cervical Vertebrae; Female; Posterior laminectomy and fusion; RD701-811; Research Article; Forecasting

Abstract Background This study aimed to predict C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables using a support vector machine (SVM) method.

Methods We conducted a retrospective investigation based on 184 consecutive patients with CM after PLF, and data were collected from March 2013 to December 2019. Clinical and imaging variables were obtained and imported into univariable and multivariable logistic regression analyses 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 184 consecutive patients, C5P occurred in 26 patients (14.13%). Multivariate analyses demonstrated the following 4 independent factors associated with C5P: abnormal electromyogram (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of C4–C5 foraminal stenosis (OR = 15.492). The SVM model achieved an area under the receiver operating characteristic curve (AUC) of 0.923 and an ACC of 0.918. Additionally, the confusion matrix showed the classification results of the discriminant analysis.

Conclusions The designed SVM model presented satisfactory performance in predicting C5P from routinely available variables. However, future external validation is needed.