Background/Purpose: Identification of SLE cases in administrative healthcare databases is used to estimate disease frequency, healthcare utilization and cost. The optimal methodology for achieving this is unclear. Our aim was to validate and compare a range of decision rules that can be applied to administrative databases to identify patients with SLE.

Methods: The study was conducted at a single academic medical center. Administrative health care data from a geographic area with approximately 1 million people who had access to universal healthcare was utilized. A retrospective cohort study was performed through the Population Health Research Unit at our institution and utilized data from existing administrative databases. These included information on hospital discharges and physician billings over a 10 year period. Each SLE study subject was matched by age and gender to 4 randomly selected control subjects in the same datasets but without a diagnosis of SLE or other connective tissue diseases. A total of 7 decision rules, some derived from previous studies, were applied to the administrative data to identify SLE cases. The sensitivity, specificity, overall accuracy, positive (PPV) and negative (NPV) predictive values of these rules was compared to the diagnosis of a rheumatologist in the academic medical center determined by chart review.

Results:

Conclusion: The performance of decision rules for the identification of SLE cases in administrative healthcare databases is variable and should be considered when comparing data across studies. This variability may be used to advantage in study design when, for example, either sensitivity or specificity is the most critical issue for different population health research questions.