• Gianpaolo Jannelli, Aria Nouri, Granit Molliqaj, Giovanni Grasso, and Enrico Tessitore.
• Neurosurgical Unit, Geneva University Hospitals, University of Geneva, Faculty of Medicine, Geneva, Switzerland. Electronic address: gianpaolo.jannelli@hcuge.ch.
• World Neurosurg. 2020 Aug 1; 140: 541-547.

AbstractDegenerative cervical myelopathy is the most common cause of spinal cord injury in the elderly population in the developed world, and it significantly affects the quality of life of patients and their caregivers. Surgery remains the only treatment option able to halt disease progression and provide neurological recovery for most patients. Although it has remained challenging to predict exactly who will experience improvement after surgery, increasingly it has been shown that clinical, imaging, and electrophysiological factors can predict, with relatively good capacity, those more likely to benefit. Clinically, the baseline neurological impairment appears to be strongly related to the outcome, and the magnetic resonance imaging findings of T1-weighted hypointensity and the length of T2-weighted hyperintensity appear to be the most prognostic. In this context, electrophysiology findings (both motor and sensory evoked potentials) have shown some predictive capacity. However, large studies are lacking. Although multivariate models have been conducted using clinical and magnetic resonance imaging data, no multimodal prediction models are available that encompass the predictive capacity of clinical, imaging, and electrophysiological data. In the present review, we examined the rationale for clinical, imaging, and electrophysiological usage in clinical practice and discussed a model of multimodal assessment for the management of degenerative cervical myelopathy.Copyright © 2020 Elsevier Inc. All rights reserved.

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