Th97 - Utilization of a Clinical Data Research Network to Improve Identification of Systemic Lupus International Collaborating Clinics Classification Criteria Attributes in Patients with Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is an autoimmune disease characterized by a heterogenous clinical phenotype that may present differently over time and between patients, which can create challenges for identification and treatment of individuals visiting multiple care centers. Clinical data research networks (CDRN) aim to pool electronic health records (EHR) to provide more complete clinical information for patients shared across care centers. We sought to identify whether differences existed in algorithms Systemic Lupus International Coordinating Clinics (SLICC) classification criteria with the inclusion of data from multiple sites. Previously published algorithms for the identification of the SLICC attributes were adapted to the PCORnet Common Data Model used by the Chicago Area Patient Centered Outcomes Research Network (CAPriCORN), initially identifying 1,231,130 persons having at least one SLICC attribute.  A subset of 1,251 individuals with data from ≥1 CAPriCORN sites and ≥ 3 instances of a SLE ICD code was retained for the primary analysis. The number of non-redundant attributes satisfied when data was included from multiple sites was quantified and compared to attributes met at the primary (most-visited) site. The average number of SLICC attributes was 3.6 per person when data was included from only one site. This increased to 4.5 when data from multiple sites were included (p < 0.0001). Rates of individual SLICC attributes were also compared, all of which were identified at significantly higher rates with multisite data inclusion. The results suggest CDRNs can be used to increase the amount of non-redundant data for increased identification of SLE attributes.