Poor Outcomes After Acute Myocardial Infarction in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is associated with higher risk for acute myocardial infarction (MI); but the post-infarction outcomes among these patients are unknown. Our objective was to compare post-acute MI outcomes in patients with SLE to those with diabetes mellitus (DM) and those with neither condition. METHODS: We analyzed the risk for prolonged hospitalization and in-hospital mortality following acute MI in the 1993-2002 US Nationwide Inpatient Sample. We used logistic regression to calculate odds ratios (OR) for prolonged hospitalization and Cox proportional hazards regression to calculate hazard ratios (HR) for in-hospital mortality with and without adjustments for age, sex, race/ethnicity, socioeconomic status, and presence of congestive heart failure. RESULTS: For the SLE (n = 2192), DM (n = 236,016), SLE/DM (n = 474), and control (n = 667,956) groups, the in-hospital mortality rates were 8.3%, 6.2%, 5.7%, and 4.7%, respectively. In multivariable regression models, all 3 disease groups had higher adverse outcome risk compared to control. The OR for prolonged hospitalization was higher for those with SLE (OR 1.48, 95% CI 1.32-1.79) compared to those with DM (OR 1.30, 95% CI 1.28-1.32). A similar pattern was observed for hazard ratios for in-hospital mortality as well (SLE, HR 1.65, 95% CI 1.33-2.04; DM, HR 1.11, 95% CI 1.07-1.14). CONCLUSION: SLE, like DM, increases risk of poor outcomes after acute MI. These patients need to be triaged appropriately for aggressive care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".