LAUSR

Cervical spondylotic myelopathy (CSM) is caused by cervical spine degeneration and surgery may be beneficial, but selection for surgery might be challenging. We performed a multimodal analysis to assess predicting factors that may be useful to help surgeons in this choice. We retrospectively evaluated clinical, motor evoked potentials (MEP), and MRI data of patients who undergone surgery for CSM. Seventy-six consecutive patients (46 males) were enrolled. The median age was 65.5 [IQR: 57–71] years, and the duration of symptoms was 11 [8–13] months. A multivariate analysis in order to assess predictors of outcome and ROC curve analysis were performed. Thirty patients (M:18, 39.5%) gained 6 or more points on mJOA and they were collected in good recovery group, whereas 46 patients (60.5%, M:28) showed a fair recovery. We developed a comprehensive score system (CSS) taking into account clinical, neurophysiological, and neuroradiological data. ROC curve analysis was performed to determine the discriminative power of four models derived from the multivariate logistic regression analysis for predictors of good outcome considering only clinical variables, MRI variables, and MEP variables or considering the comprehensive model, demonstrating a good accuracy of CSS model to predict outcome. This study demonstrates that CSS model taking into consideration functional assessment by mJOA score, neurologic evaluation, cervical MRI, and MEP may be a feasible method to predict outcome in patients candidate to surgery, supporting surgeon’s decisions both for those patients candidate to surgery and for patients in whom a “wait and see” approach could be proposed.