Outcomes of ST-Segment Elevation Myocardial Infarction in Patients With Adrenal Insufficiency
Bibliographic record
Abstract
Abstract Context Patients with adrenal insufficiency (AI) have both increased risk of cardiovascular disease and adverse outcomes with many medical emergencies. However, limited data exist specifically regarding ST-segment elevation myocardial infarction (STEMI) in the context of AI. Objective To evaluate associations between AI and in-hospital outcomes of patients with STEMI. Methods Admissions for STEMI were identified in the 2016-2019 National Inpatient Sample. In-hospital outcomes were compared between patients with and without AI. The primary outcome was in-hospital mortality. Secondary outcomes included percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), intervention, acute kidney injury (AKI), vasopressor use, mechanical circulatory support (MCS), mechanical ventilation, ventricular tachycardia (VT), hospital length of stay (LOS), and total charges. Multivariable regression models were used to adjust for potential confounders. Results Among 690 430 STEMI hospitalizations, 1382 (0.2%) had a diagnosis of AI. AI was associated with higher odds of in-hospital mortality (adjusted OR [aOR] 1.51, 95% CI 1.03-2.2), lower odds of PCI (aOR 0.73, 95% CI 0.55-0.98), higher odds of CABG (aOR 2.8, 95% CI 1.89-4.2) and, AKI (aOR 2.38, 95% CI 1.72-3.3), VT (aOR 1.55, 95% CI 1.1-2.2), need for vasopressors (aOR 2.34, 95% CI 1.33-4.1), mechanical ventilation (aOR 2.11, 95% CI 1.54-2.89), and MCS (aOR 2.18, 95% CI 1.57-3.03). Patients with AI also had a longer LOS (10 days vs 4.2 days, P < .001) and higher charges ($258 475 vs $115 505, P < .001). Conclusion Patients with AI admitted for STEMI had higher in-hospital mortality, nonfatal adverse outcomes, and resource utilization than patients without AI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".