SLE: Variability in Racial Disparities in Pregnancy Outcomes Save
Significant disparities exist in pregnancy outcomes in women with systemic lupus erythematosus (SLE), with previous cohorts identifying Black women as having a higher risk of maternal mortality compared to White women with SLE. However, there are few diverse datasets available that allow for extensive analysis of factors associated with these disparities, such as race.
To examine differences in pregnancy outcomes based on different algorithm definitions of SLE pregnancies, Clowse et al utilized the Carolinas Collaborative electronic medical record (EMR)-based datasets to identify women with pregnancy delivery data and SLE ICD-9 and ICD-10 codes for SLE (abstract 0962).
Previously, this group identified an EMR-based algorithm for SLE diagnosis that it is more accurate in Black women compared to White. At #ACR22 they now applied four separate algorithm definitions of SLE pregnancies, ultimately establishing four cohorts of patients with SLE: three cohorts identified via algorithms, and one identified via chart review (true positive).
In total, 172 pregnancies in women were identified to have at least one SLE ICD-9 or ICD-10 code. Of these pregnancies, only 49- 55% were identified by the three algorithms, and 49% by chart review. Adverse pregnancy outcomes were identified in 40% of pregnancies in women and 52% of pregnancies with confirmed SLE on chart review. The adverse pregnancy outcome rate in the true positives was higher than what was reported in previous cohorts.
Over-diagnosis of SLE was much more common in White women than in Black women. This results in a lot of under-estimations of pregnancy risk in White women. Accordingly, despite data illustrating higher rates of adverse pregnancy outcomes in Black women than White women, these disparities were not significant in the cohorts confirmed through chart review. Clowse et al demonstrated that more accurate labelling for SLE, as was identified in Black women, led to more accurate estimates of pregnancy risk. This suggests that differences in algorithm performance based on race can underestimate or overestimate risk. Also, prediction modeling in the future should account for these differences.
This study reemphasizes the importance of adequately accounting for race in lupus pregnancy outcomes in order the manage these patients clinically as well in health policy for these patients.