To determine the accuracy of case definitions for autoinflammatory syndromes (AISs) based on administrative claims codes compared with rheumatology records in the electronic medical record (EMR).
An AIS screening filter of administrative codes was applied to a large tertiary care EMR database to extract all possible AIS cases. We manually chart reviewed all patients who were evaluated by a rheumatologist to determine their reference standard diagnosis of adult onset Still's disease (AOSD), Behçet's disease (BD), and familial Mediterranean fever (FMF). We calculated sensitivity, specificity, positive predictive values, negative predictive values, and area under the receiver operating characteristic curve of specific codes for diagnosing AIS subtypes.
We identified 273 individuals with possible AIS, of which 72 (26.4%) had a true AIS diagnosis, including 24 with AOSD, 32 with BD, and 9 with FMF. For all 3 AIS subtypes, the estimates of specificities and negative predictive values for specific administrative codes were excellent (>95%). Sensitivity estimates were excellent (>89%) for BD and FMF codes and lower for AOSD (46%-50%). Positive predictive values were excellent for BD (>99%) and AOSD (>86%) and lower for FMF (>53%). Area under the receiver operating characteristic curve estimates were excellent for BD (97%-98%) and FMF (93%) and very good for AOSD (75%).
This is the first study to characterize the accuracy of specific administrative codes for the diagnosis of AOSD, BD, and FMF in a large tertiary care EMR. Validation in external EMRs and linked EMR-administrative databases is needed to enable future clinical outcomes research of AIS.