SEASONAL VARIATION IN MORTALITY AND CARDIOVASCULAR OUTCOMES AMONG HOSPITALIZED SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS: A NATIONWIDE ANALYSIS
Bibliographic record
Abstract
PV089 / #330 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose While seasonal variation affects many autoimmune conditions, its impact on hospitalized Systemic Lupus Erythematosus (SLE) patients remains poorly understood. We aimed to evaluate seasonal patterns in mortality and major clinical outcomes among hospitalized SLE patients 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). Seasons were defined as Spring (March-May), Summer (June-August), Fall (September-November), and Winter (December-February). Primary outcomes included in-hospital mortality, cardiac arrest, and respiratory failure requiring intubation. We employed survey-weighted multivariable logistic regression to calculate adjusted odds ratios (aOR), controlling for age, sex, race, Elixhauser comorbidity index, hypertension, diabetes, chronic kidney disease, prior myocardial infarction, and prior stroke. Results Among 170,085 hospitalized SLE patients, we observed even seasonal distribution (Spring 25.85%, Summer 25.94%, Fall 24.43%, Winter 23.78%). Unadjusted mortality rates demonstrated a progressive increase from Spring (2.36%) through Summer (2.89%) and Fall (3.35%), peaking in Winter (3.59%). After adjusting for demographics and comorbidities, this pattern persisted with significantly increased risk in Summer (aOR 1.24, 95% CI 1.03-1.49, p = 0.025), Fall (aOR 1.41, 95% CI 1.18-1.70, p < 0.001), and Winter (aOR 1.54, 95% CI 1.28-1.87, p < 0.001) compared to Spring. Cardiac arrest risk was highest in Summer and Winter (both aOR 1.54, 95% CI 1.13-2.08, p = 0.006 and 95% CI 1.14-2.09, p = 0.005, respectively). Respiratory failure requiring intubation showed a similar pattern, with risk increasing through Fall (aOR 1.22, 95% CI 1.02-1.45, p = 0.026) and peaking in Winter (aOR 1.34, 95% CI 1.12-1.59, p = 0.001). Mean length of stay increased from Spring (5.54 days, 95% CI 5.40-5.69) to Fall (5.93 days, 95% CI 5.78-6.08), with Fall showing significantly higher total hospital charges compared to Spring (adjusted incidence rate ratio [aIRR] 1.08, 95% CI 1.03-1.14, p = 0.003). Conclusions This first comprehensive analysis of seasonal variation in SLE hospitalizations reveals clinically and statistically significant increases in mortality and adverse outcomes during Fall/Winter months, with effect sizes ranging from 24% to 54% increased odds of mortality. These findings suggest the need for heightened vigilance and potentially adjusted monitoring strategies during higher-risk seasons.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".