title: Validation of an administrative coding algorithm for classifying surgical indication and operative features of spine surgery.
abstract: METHODS
  Retrospective review of medical records and administrative data.
  
  
  OBJECTIVE
  Validate a claims-based algorithm for classifying surgical indication and operative features in lumbar surgery.
  
  
  BACKGROUND
  Administrative data are valuable to study rates, safety, outcomes, and costs in spine surgery. Previous research evaluates outcomes by procedure, not indications and operative features. One previous study validated a coding algorithm for classifying surgical indication. Few studies examined claims data for classifying patients by operative features.
  
  
  METHODS
  Patients undergoing lumbar decompression or fusion at a single institution in 2009 for back pain, herniated disc, stenosis, spondylolisthesis, or scoliosis were included. Sensitivity and specificity of a claims-based algorithm for indication and operative features were examined versus medical record abstraction.
  
  
  RESULTS
  A total of 477 patients, including 246 (52%) undergoing fusion and 231 (48%) undergoing decompression were included in this study. Sensitivity of the claims-based coding algorithm for classifying the indication for the procedure was 71.9% for degenerative disc disease, 81.9% for disc herniation, 32.7% for spinal stenosis, 90.4% for degenerative spondylolisthesis, and 93.8% for scoliosis. Specificity was 87.9% for degenerative disc, 85.6% for disc herniation, 90.7% for spinal stenosis, 95.0% for degenerative spondylolisthesis, and 97.3% for scoliosis. Sensitivity and specificity of claims data for identifying the type of procedure for fusion cases was 97.6% and 99.1%, respectively. Sensitivity of claims data for characterizing key operative features was 81.7%, 96.4%, and 53.0% for use of instrumentation, combined (anterior and posterior) surgical approach, and 3 or more disc levels fused, respectively. Specificity was 57.1% for instrumentation, 94.5% for combined approaches, and 71.9% for 3 or more disc levels fused.
  
  
  CONCLUSIONS
  Claims data accurately reflected certain diagnoses and type of procedures, but were less accurate at characterizing operative features other than the surgical approach. This study highlights both the potential and current limitations of claims-based analysis for spine surgery.
authors.affiliation: *Harvard Medical School, Boston, MA †Department of Orthopaedic Surgery, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, NH ‡Center for Healthcare Delivery Science, Pulmonary and Critical Care, Beth Israel Deaconess Medical Center, Boston, MA; and §Department of Orthopedic Surgery, Center for Health Care Delivery Science, Beth Israel Deaconess Medical Center, Boston, MA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Kazberouk
authors.forename: Brook I
authors.initials: BI
authors.lastname: Martin
authors.forename: Jennifer P
authors.initials: JP
authors.lastname: Stevens
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: McGuire
mesh_terms: D000465:Algorithms; D059019:Clinical Coding; D019299:Decompression, Surgical; D017586:Diskectomy; D006801:Humans; D008159:Lumbar Vertebrae / Q000601:surgery*; D012189:Retrospective Studies; D013122:Spinal Diseases / Q000601:surgery*; D013123:Spinal Fusion