Abstract

Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord resulting from vertebral column spondylotic degeneration. Typical neurological symptoms of DCM include gait imbalance, hand/arm numbness, and upper extremity dexterity loss. Greater spinal cord compression is believed to lead to a higher rate of neurological deterioration, although clinical experience suggests a more complex mechanism involving spinal canal diameter (SCD). In this study, we utilized machine learning clustering to understand the relationship between SCD and different patterns of cord compression (i.e. compression at one disc level, two disc levels, etc.) to identify patient groups at risk of neurological deterioration. 124 MRI scans from 51 non-operative DCM patients were assessed through manual scoring of cord compression and SCD measurements. Dimensionality reduction techniques and k-means clustering established patient groups that were then defined with their unique risk criteria. We found that the compression pattern is unimportant at SCD extremes (≤14.5 mm or > 15.75 mm). Otherwise, severe spinal cord compression at two disc levels increases deterioration likelihood. Notably, if SCD is normal and cord compression is not severe at multiple levels, deterioration likelihood is relatively reduced, even if the spinal cord is experiencing compression. We elucidated five patient groups with their associated risks of deterioration, according to both SCD range and cord compression pattern. Overall, SCD and focal cord compression alone do not reliably predict an increased risk of neurological deterioration. Instead, the specific combination of narrow SCD with multi-level focal cord compression increases the likelihood of neurological deterioration in mild DCM patients.

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