Time Trends in Acute Coronary Syndrome Hospitalizations and Outcomes in Patients With Systemic Lupus Erythematosus: A United States Inpatient Cohort Analysis
Bibliographic record
Abstract
Objective Systemic lupus erythematosus (SLE) is an autoimmune disease linked to higher cardiovascular risks, such as acute coronary syndrome (ACS). Limited real-world data exist on ACS outcomes in patients with SLE. This study examines trends in ACS hospitalizations among patients with SLE (2006-2019) and compares outcomes and healthcare utilization between ACS patients with and without SLE. Methods Using data from the US National Inpatient Sample from 2006 to 2019, ACS hospitalizations were classified by the presence or absence of SLE using International Classification of Diseases (ICD), 9th (ICD-9) and 10th revisions (ICD-10) codes. Hospitalization rates, mortality, length of stay, and charges were compared between the 2 groups. Chi-square and t tests assessed associations with SLE for categorical and continuous variables, respectively, with P < 0.05 as the significance threshold. Results Of 17,318,554 ACS hospitalizations, 70,882 involved patients with SLE, who were more often < 50 years of age, female, Black, and had higher rates of antiphospholipid syndrome, chronic and endstage kidney disease, and prior thromboembolism. From 2006 to 2019, ACS hospitalization rates fell by 40% in patients with SLE—mainly from 2015 to 2019—and by 50% in patients without SLE. In-hospital mortality was similar (7% vs 6.9%, P = 0.52), though patients with SLE experienced longer hospital stays (6.22 vs 5.51 days, P < 0.001) and higher charges (US $79,909 vs $74,294, P < 0.001). Conclusion SLE patients with ACS require higher healthcare utilization, with longer hospital stays and higher charges. Although ACS hospitalization rates declined for both groups, the decrease was greater in patients without SLE. These findings underscore the need for continuous targeted cardiovascular risk management strategies in patients with SLE to reduce morbidity and healthcare burden.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".