ECONOMIC IMPACT AND RESOURCE UTILIZATION OF CORONAVIRUS DISEASE 2019 IN HOSPITALIZED SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS: A NATIONAL INPATIENT ANALYSIS
Bibliographic record
Abstract
PV091 / #536 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose The economic impact of Coronavirus Disease 2019 (COVID-19) in Systemic Lupus Erythematosus (SLE) patients remains incompletely characterized. We aimed to analyze healthcare resource utilization and costs among hospitalized SLE patients with COVID-19 compared to those without, using a nationally representative database. Methods Using the 2021 National Inpatient Sample, we identified adult SLE patients (International Classification of Diseases, 10th Revision code M32) and stratified by COVID-19 status. Primary outcomes included length of stay (LOS) and total hospital charges. We employed survey-weighted Poisson regression for LOS analysis and generalized linear models for cost analysis, adjusting for demographics, comorbidities, and clinical factors. Results Among 170,085 hospitalized SLE patients, 12,710 (7.47%) had COVID-19. Mean LOS was significantly longer in SLE patients with COVID-19 vs without (8.48 vs 5.57 days, p < 0.001). After adjustment, COVID-19 was associated with a 53% increase in LOS (incidence rate ratio [IRR] 1.53, 95% CI 1.47-1.60, p < 0.001). Total hospital charges were substantially higher in COVID-19 patients vs non-COVID-19 patients ($103,126 vs $77,628, p < 0.001), with an adjusted cost ratio of 1.34 (95% CI 1.26-1.42, p < 0.001). Stratified analyses revealed higher rates of mechanical ventilation (11.25% vs 2.76%, p < 0.001) and acute kidney injury (29.31% vs 21.38%, p < 0.001) in COVID-19 patients. Among SLE patients with COVID-19, females vs males (88.24% vs 11.76%) showed lower hospital charges (IRR 0.91, 95% CI 0.85-0.98, p = 0.015) and shorter LOS (IRR 0.92, 95% CI 0.88-0.96, p < 0.001). African American vs Caucasian patients (29.50% vs 48.03%) demonstrated higher resource utilization (IRR 1.29, 95% CI 1.23-1.36, p < 0.001). Patients with severe vs mild comorbidity burden (41.03% vs 9.87%) had significantly higher costs (IRR 2.15, 95% CI 2.03-2.28, p < 0.001). Conclusions COVID-19 in SLE patients is associated with substantially increased healthcare resource utilization, manifesting as longer hospitalizations and higher costs. The economic burden is particularly pronounced in patients requiring mechanical ventilation or developing acute kidney injury. These findings highlight the significant economic impact of COVID-19 in SLE patients and can inform healthcare resource allocation and policy decisions for 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".