Etiology-Based Classification of Adjacent Segment Disease Following Lumbar Spine Fusion

Background

Adjacent segment disease (ASDz) is a potential complication following lumbar spinal fusion. A common nomenclature based on etiology and ASDz type does not exist and is needed to assist with clinical prognostication, decision making, and management.

Questions/Purposes

The objective of this study was to develop an etiology-based classification system for ASDz following lumbar fusion.

Methods

We conducted a retrospective chart review of 65 consecutive patients who had undergone both a lumbar fusion performed by a single surgeon and a subsequent procedure for ASDz. We established an etiology-based classification system for lumbar ASDz with the following six categories: “degenerative” (degenerative disc disease or spondylosis), “neurologic” (disc herniation, stenosis), “instability” (spondylolisthesis, rotatory subluxation), “deformity” (scoliosis, kyphosis), “complex” (fracture, infection), or “combined.” Based on this scheme, we determined the rate of ASDz in each etiologic category.

Results

Of the 65 patients, 27 (41.5%) underwent surgery for neurogenic claudication or radiculopathy for adjacent-level stenosis or disc herniation and were classified as “neurologic.” Ten patients (15.4%) had progressive degenerative disc pathology at the adjacent level and were classified as “degenerative.” Ten patients (15.4%) had spondylolisthesis or instability and were classified as “instability,” and three patients (4.6%) required revision surgery for adjacent-level kyphosis or scoliosis and were classified as “deformity.” Fifteen patients (23.1%) had multiple diagnoses that included a combination of categories and were classified as “combined.”

Conclusion

This is the first study to propose an etiology-based classification scheme of ASDz following lumbar spine fusion. This simple classification system may allow for the grouping and standardization of patients with similar pathologies and thus for more specific pre-operative diagnoses, personalized treatments, and improved outcome analyses.