PREDICTORS OF MECHANICAL VENTILATION IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS WITH CORONAVIRUS DISEASE 2019: INSIGHTS FROM NATIONAL INPATIENT DATA
Bibliographic record
Abstract
PV052 / #534 Poster Topic: AS06 - Comorbidities Background/Purpose Mechanical ventilation represents a critical outcome in Systemic Lupus Erythematosus (SLE) patients with Coronavirus Disease 2019 (COVID-19). We aimed to identify specific risk factors and predictors of mechanical ventilation in this population using nationally representative data. Methods Analyzing the 2021 National Inpatient Sample, we identified adult SLE patients with COVID-19 (ICD-10 code M32). The primary outcome was mechanical ventilation. Using survey-weighted logistic regression, we calculated adjusted odds ratios (aOR) for ventilation risk, controlling for demographics, comorbidities, and clinical factors. Results Among 170,085 SLE patients, 12,710 (7.47%) had COVID-19. The mechanical ventilation rate was significantly higher in COVID-19 patients vs non-COVID-19 (11.25% vs 2.76%, p < 0.001). After adjustment, COVID-19 remained strongly associated with ventilation risk (aOR 4.87, 95% CI 4.22-5.62, p < 0.001). Demographic analysis revealed higher ventilation rates in males vs females (11.76% vs 8.82%, aOR 1.06, 95% CI 0.79-1.12, p = 0.472) and African American vs Caucasian patients (13.3% vs 9.8%, aOR 1.17, 95% CI 1.00-1.37, p = 0.038). Comorbidity burden strongly predicted ventilation need: severe vs mild Elixhauser index (41.03% vs 9.87%, aOR 11.80, 95% CI 8.06-17.29, p < 0.001). Notable comorbidity associations included obesity (31.94% vs 22.23%, aOR 1.21, 95% CI 1.10-1.37, p < 0.001), diabetes (30.02% vs 23.54%, aOR 1.18, 95% CI 1.09-1.28, p < 0.001), and hypertension (34.70% vs 30.56%, aOR 0.60, 95% CI 0.51-0.70, p < 0.001). Acute complications significantly associated with ventilation included acute kidney injury (29.31% vs 21.38%, aOR 2.47, 95% CI 2.31-2.65, p < 0.001) and cardiac arrest (2.64% vs 0.92%, aOR 2.98, 95% CI 2.28-3.90, p < 0.001). Conclusions This analysis identifies key predictors of mechanical ventilation in SLE patients with COVID-19, highlighting the complex interplay between demographics, comorbidities, and acute complications. The nearly 5-fold increased risk of ventilation in COVID-19 patients, along with identified risk factors, can inform clinical decision making and resource allocation for this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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".