Validation of Using Claims Data to Measure Safety of Lumbar Fusion Surgery

Retrospective analysis of patients undergoing elective lumbar fusion operations, comparing rates of repeat spine surgery based on method of ascertainment. We report the accuracy of a claims-based approach for reporting repeat surgery compared to medical records abstraction as the “gold standard.” Previous studies have reported the validity of a claims-based algorithm for grouping patients by surgical indication and classifying operative features, but their accuracy in measuring surgical quality indicators has not been widely examined. We identified a subset of patients undergoing elective lumbar fusion operations at a single institution from 1996–2011, excluding those with spinal fracture, spinal cord injury, or cancer. From the medical record we abstracted the incidence of repeat spine operation or re-hospitalization at one-year. We cross-classified each event record with its corresponding value derived from claims. The sensitivity and specificity of the claims-based approach were calculated for reoperation within 30, 90, and 365 days, and all-cause hospital readmission within 30 days. Medical records linked to claims data were obtained for 520 patients undergoing elective lumbar fusion. Reoperation rates based on chart review were 1.0%, 1.3%, 3.6%, compared to 0.8%, 1.7%, and 3.8% based on the final claims methods at 30, 90, and 365 days, respectively. The claims-based algorithm had sensitivities of 80.0%, 100%, and 94.1% and specificities of 100%, 99.6%, 99.2% for repeat surgery within 30, 90, and 365 days, respectively. The sensitivity for all-cause readmission was 50%. Health care quality improvement efforts often rely on administrative data to report surgical safety. We found that claims-based ascertainment of safety at a single institution was very accurate. However, accuracy depended on careful attention to the timing of outcomes, as well as the definitions and coding of repeat surgery, including how orthopaedic device removal codes are classified. Level of Evidence: 3