Defining major surgical complications using administrative data in Ontario: a validation study

Background — Although surgical complications are often included as an outcome of surgical research conducted using administrative data, little validation work has been performed. We sought to evaluate the diagnostic performance of an algorithm designed to capture major surgical complications using health administrative data.

Methods — This retrospective study included patients who underwent high-risk elective general surgery at a single institution in Ontario, Canada, from Sept. 1, 2016, to Sept. 1, 2017. Patients were identified for inclusion using the local operative database. Medical records were reviewed by trained clinicians to abstract postoperative complications. Data were linked to administrative data holdings, and a series of code-based algorithms were applied to capture a composite indicator of major surgical complications. We used sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy to evaluate the performance of our administrative data algorithm, as compared with data abstracted from the institutional charting system.

Results — The study included a total of 270 patients. According to the data from the chart audit, 55% of patients experienced at least 1 major surgical complication. Overall sensitivity, specificity, PPV, NPV and accuracy for the composite outcome was 72%, 80%, 82%, 70% and 76%, respectively. Diagnostic performance was poor for several of the individual complications.

Conclusion — Our results showed that administrative data holdings can be used to capture a composite indicator of major surgical complications with adequate sensitivity and specificity. Additional work is required to identify suitable algorithms for several specific complications.