ICD-10 Coding of Cervical Radiculopathy Underestimates Concurrent Myelopathy

Retrospective cohort study.

To evaluate the accuracy of ICD-10 codes in identifying isolated degenerative cervical radiculopathy versus concurrent myelopathy in surgical patients across an 11-hospital health care system (2016-2023).

ICD-10 codes are commonly used for patient stratification in database studies, yet their accuracy in differentiating cervical radiculopathy from concurrent myelopathy remains poorly characterized. This distinction is clinically critical as treatment approaches and outcomes differ significantly between these conditions.

We reviewed 830 surgical patients identified using ICD-10 codes for cervical radiculopathy (M50.10-50.13, M54.11-M54.13, or M25.78). Patients with codes for myelopathy, traumatic, oncologic, or infectious etiologies were excluded. Primary diagnoses were determined through standardized chart review by 3 independent clinical reviewers blinded to ICD-10 codes. Patients were classified using specific criteria: radiculopathy (dermatomal symptoms, positive provocative tests, and foraminal stenosis) and myelopathy (upper motor neuron signs, gait disturbances, fine motor deficits, and cord signal changes on MRI).

Chart review revealed 637 patients (76.7%) had isolated cervical radiculopathy, confirming ICD-10 coding. However, 151 patients (18.2%) presented with concurrent myelopathic symptoms (myeloradiculopathy), and 35 patients (4.2%) demonstrated isolated cervical myelopathy, despite being coded only for radiculopathy. Nondegenerative etiologies comprised 0.8% of cases. The nonspecific code M25.78 showed higher error rates (26%) compared with specific codes, though M54.12 still demonstrated a 22% error rate. Code position significantly influenced accuracy, with error rates increasing from 14% (primary position) to 43% (fourth position).

A significant proportion of patients coded for isolated cervical radiculopathy have concurrent myelopathy that goes unrecognized with ICD-10 coding alone. Code specificity and diagnostic position significantly impact accuracy. Future strategies, including decision support tools and natural language processing, may improve coding accuracy.

Level III.