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Purpose
To investigate the prevalence of thoracic scoliosis and determine the effect of both age and gender on coronal curve magnitude among asymptomatic adults aged 25–64 years old, using standing posterior-anterior chest radiographs.Methods
This was a retrospective, cross-sectional study evaluating 500 randomly selected digital posterior-anterior chest radiographs taken at a single institution on an outpatient basis between January 2010 and December 2011. Males (n = 184) and females (n = 316) ranged in age from 25 to 64 years. Patients with symptoms of back pain; including a history of back pain, spinal instrumentation, or known pre-existing spinal disease were excluded. Radiographs were evaluated using Centricity PACS Web Diagnostic 2.1 system (General Electric Co. Fairfield, CT). Coronal Cobb angle measurements of the thoracic spine were quantified by the authors, with scoliosis defined as coronal curves greater than 10°. Curvatures were subdivided into groups: a control group with coronal curves less than 10°, curves measuring 10° to 19°, 20° to 29°, and greater than 30°. The effect of age and gender on curve magnitude was examined using Pearson correlation analysis and linear regression analysis.Results
There was a 13.4 % (67 patients) prevalence of thoracic scoliosis. The prevalence among asymptomatic males was 10.9 %, while the prevalence among asymptomatic females was 14.9 %. 11.6 % demonstrated a coronal curvature between 10° and 19° (58 patients), 1.6 % between 20° and 29° (8 patients), and 0.2 % greater than 30° (1 patient). Age and gender were not found to be significant independent predictors of curve severity.Conclusions
We found a 13.4 % prevalence of thoracic scoliosis among asymptomatic adults aged 25–64 years on routine outpatient chest radiographs. 11.6 % of patients demonstrated a coronal curvature between 10° and 19°. Unlike prior studies evaluating asymptomatic thoracic curves in elderly patients, age and gender did not significantly affect curve magnitude in our younger cohort. These data may provide a reference point to help clinicians counsel asymptomatic patients diagnosed with thoracic scoliosis on routine chest radiographs.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/PurposesThe objective of this study was to develop an etiology-based classification system for ASDz following lumbar fusion.
MethodsWe 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.
ResultsOf 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.”
ConclusionThis 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.
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