Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network

Abstract

Objective: To assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network.

Methods: Using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a Clinical Data Research Network (CDRN) containing data from multiple healthcare sites, we identified patients with at least one positively identified criterion from three SLE classification criteria sets developed by the American College of Rheumatology (ACR) in 1997, the Systemic Lupus International Collaborating Clinics (SLICC) in 2012, and the European Alliance of Associations for Rheumatology and the ACR in 2019 using EHR-based algorithms. To measure the algorithms' performance in this data setting, we first evaluated whether the number of clinical encounters for SLE was associated with a greater quantity of positively identified criteria domains using Poisson regression. We next quantified the amount of SLE criteria identified at a single healthcare institution versus all sites to assess the amount of SLE-related information gained from implementing the algorithms in a CDRN.

Results: Patients with three or more SLE encounters were estimated to have documented 2.77 (2.73 to 2.80) times the number of positive SLE attributes from the 2012 SLICC criteria set than patients without an SLE encounter via Poisson regression. Patients with three or more SLE-related encounters and with documented care from multiple institutions were identified with more SLICC criteria domains when data were included from all CAPriCORN sites compared with a single site (p<0.05).

Conclusions: The positive association observed between amount of SLE-related clinical encounters and the number of criteria domains detected suggests that the algorithms used in this study can be used to help describe SLE features in this data environment. This work also demonstrates the benefit of aggregating data across healthcare institutions for patients with fragmented care.

Keywords: Epidemiology; Health services research; Lupus Erythematosus, Systemic.

Figure 1

Cohort identification and partitioning. Algorithms designed to detect attributes from the 1997 ACR, 2012 SLICC, 2019 EULAR/ACR classification criteria sets were implemented at all CAPriCORN sites containing a validated PCORnet common data model datamart at the time of data capture. Any patient for which the algorithms identified one or more positive attributes from the three criteria sets was included for subsequent partitioning based on the number of SLE encounters identified during the study period. ACR, American College of Rheumatology; CAPriCORN, Chicago Area Patient-Centered Outcomes Research Network; CDRN, Clinical Data Research Network; EULAR, European Alliance of Associations for Rheumatology; PCORnet, Patient Centered Outcomes Research Network; SLICC, Systemic Lupus International Collaborating Clinics.