Predicting Outcomes After Surgical Decompression for Mild Degenerative Cervical Myelopathy: Moving Beyond the mJOA to Identify Surgical Candidates

BACKGROUND: Patients with mild degenerative cervical myelopathy (DCM) represent a heterogeneous population, and indications for surgical decompression remain controversial. OBJECTIVE: To dissociate patient phenotypes within the broader population of mild DCM associated with degree of impairment in baseline quality of life (QOL) and surgical outcomes. METHODS: This was a post hoc analysis of patients with mild DCM (modified Japanese Orthopedic Association [mJOA] 15-17) enrolled in the AOSpine CSM-NA/CSM-I studies. A k-means clustering algorithm was applied to baseline QOL (Short Form-36 [SF-36]) scores to separate patients into 2 clusters. Baseline variables and surgical outcomes (change in SF-36 scores at 1 yr) were compared between clusters. A k-nearest neighbors (kNN) algorithm was used to evaluate the ability to classify patients into the 2 clusters by significant baseline clinical variables. RESULTS: One hundred eighty-five patients were eligible. Two groups were generated by k-means clustering. Cluster 1 had a greater proportion of females (44% vs 28%, P = .029) and symptoms of neck pain (32% vs 11%, P = .001), gait difficulty (57% vs 40%, P = .025), or weakness (75% vs 59%, P = .041). Although baseline mJOA correlated with neither baseline QOL nor outcomes, cluster 1 was associated with significantly greater improvement in disability (P = .003) and QOL (P < .001) scores following surgery. A kNN algorithm could predict cluster classification with 71% accuracy by neck pain, motor symptoms, and gender alone. CONCLUSION: We have dissociated a distinct patient phenotype of mild DCM, characterized by neck pain, motor symptoms, and female gender associated with greater impairment in QOL and greater response to surgery.