ANALYSIS OF RACIAL DISPARITIES IN CORONAVIRUS DISEASE 2019 OUTCOMES AMONG HOSPITALIZED SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS: A NATIONAL INPATIENT STUDY
Bibliographic record
Abstract
PV092 / #527 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose While racial disparities exist in both Systemic Lupus Erythematosus (SLE) and Coronavirus Disease 2019 (COVID-19) independently, their combined impact remains understudied. We aimed to analyze differences in clinical outcomes and complications across racial groups among hospitalized SLE patients with COVID-19. Methods Using the 2021 National Inpatient Sample, we identified adult SLE patients with COVID-19. We analyzed outcomes across racial categories using survey-weighted regression models, adjusting for demographics, comorbidities, and socioeconomic factors. Primary outcomes included mortality, mechanical ventilation, acute kidney injury (AKI), and length of stay (LOS). Results Among 12,710 SLE patients with COVID-19, the racial distribution was: African American (29.50%), Caucasian (48.03%), Hispanic (14.59%), Asian (1.69%), and Other (6.18%). Compared to Caucasian patients, African American patients showed higher rates of mechanical ventilation (13.3% vs 9.8%, p < 0.001), AKI (33.2% vs 27.4%, p < 0.001), and longer mean LOS (9.2 vs 7.8 days, p < 0.001). After adjustment, African American patients maintained higher odds of mechanical ventilation (adjusted odds ratio [aOR] 1.17, 95% CI 1.00-1.37, p = 0.045) and AKI (aOR 1.18, 95% CI 1.09-1.28, p < 0.001). Hispanic patients compared to Caucasian patients demonstrated increased odds of mechanical ventilation (12.4% vs 9.8%, aOR 1.13, 95% CI 1.04-1.23, p = 0.038). Notably, comorbidity patterns differed significantly: African American vs Caucasian patients had higher rates of hypertension (34.7% vs 28.9%, p < 0.001) and obesity (31.9% vs 25.8%, p < 0.001). Healthcare resource utilization also varied, with African American patients experiencing higher total charges compared to Caucasian patients ($112,340 vs $98,450, p < 0.001). Social determinants analysis revealed disparities in insurance status, with African American patients more likely to have Medicaid compared to Caucasian patients (32.4% vs 24.6%, p < 0.001). Conclusions This comprehensive analysis reveals significant racial disparities in COVID-19 outcomes among hospitalized SLE patients, with African American patients experiencing higher rates of complications and resource utilization. These findings highlight the need for targeted interventions addressing both clinical and socioeconomic factors to improve outcomes across all racial groups in this high-risk population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".