Implications of the accuracy of diagnostic algorithms for systemic lupus on our understanding of racial disparities in pregnancy outcomes.

OBJECTIVE: Disparities in pregnancy outcomes among women with SLE remain understudied, with few available racially diverse datasets. We sought to identify disparities between Black and White women in pregnancy outcomes within academic institutions in the United States. METHODS: Using the Common Data Model electronic medical record (EMR)-based datasets within the Carolinas Collaborative, we identified women with pregnancy delivery data (2014-2019) and ≥1 SLE International Classification of Diseases 9 or 10 code (ICD9/10) code. From this dataset, we identified four cohorts of SLE pregnancies, three based on EMR-based algorithms and one confirmed with chart review. We compared the pregnancy outcomes identified in each of these cohorts for Black and White women. RESULTS: Of 172 pregnancies in women with ≥1 SLE ICD9/10 code, 49% had confirmed SLE. Adverse pregnancy outcomes occurred in 40% of pregnancies in women with ≥1 ICD9/10 SLE code and 52% of pregnancies with confirmed SLE. SLE was frequently over-diagnosed in women who were White, resulting in 40-75% lower rates of adverse pregnancy outcomes in EMR-derived vs confirmed SLE cohorts. Over-diagnosis was less common for Black women with pregnancy outcomes 12-20% lower in EMR-derived vs confirmed SLE cohorts. Black women had higher rates of adverse pregnancy outcomes than White women in the EMR-derived, but not the confirmed cohorts. CONCLUSION: EMR-derived cohorts of pregnancies in women who are Black, but not White, provided accurate estimations of pregnancy outcomes. The data from the confirmed SLE pregnancies suggest that all women with SLE, regardless of race, referred to academic centres remain at very high risk for adverse pregnancy outcome.

Developing and Validating Methods to Assemble Systemic Lupus Erythematosus Births in the Electronic Health Record.

OBJECTIVE: Electronic health records (EHRs) represent powerful tools to study rare diseases. Our objective was to develop and validate EHR algorithms to identify systemic lupus erythematosus (SLE) births across centers. METHODS: We developed algorithms in a training set using an EHR with over 3 million subjects and validated the algorithms at 2 other centers. Subjects at all 3 centers were selected using ≥1 code for SLE International Classification of Diseases, Ninth Revision (ICD-9) or SLE International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) and ≥1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥1:160, and ever checked double-stranded DNA or complement, using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance. RESULTS: Algorithms performed similarly across all 3 centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American versus White SLE births. Using machine learning methods, the total number of SLE codes and an SLE code from a rheumatologist were the most important variables in the model for SLE case status. CONCLUSION: We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than in White mothers.