Influence of cardiac arrest and SCAI shock stage on cardiac intensive care unit mortality
Bibliographic record
Abstract
BACKGROUND: Patients with concomitant cardiac arrest (CA) and shock are at increased risk of mortality, even when stratified according to shock severity. We sought to determine whether the presence of ventricular fibrillation (VF) modified the relationship between CA and mortality in cardiac intensive care unit (CICU) patients. METHODS: We retrospectively analyzed unique Mayo Clinic CICU patients admitted between 2007 and 2015. Society for Cardiovascular Angiography and Intervention (SCAI) shock stages A through E were classified at admission. Hospital mortality in each SCAI shock stage was stratified by the presence of CA, VF CA, or non-VF CA. RESULTS: We included 9,898 patients with a mean age of 68 years (38% females). CA was present in 12%, including 53% with VF CA and 47% with non-VF CA. Hospital mortality was higher in patients with CA compared to patients without CA (34% vs. 6%; adjusted odds ratio [OR] = 3.1, 95% CI [2.4, 4.0], p < .001), and patients with non-VF CA had higher hospital mortality than patients with VF CA (44% vs. 25%; adjusted OR = 2.1, 95% CI [1.4, 3.0], p < .001). After adjustment, patients with any CA or non-VF CA had higher hospital mortality at each SCAI stage, except stage E (all other p < .05), whereas patients with VF CA did not (all p > .1). CONCLUSIONS: CA rhythm modifies the relationship between CA and mortality in CICU patients, when accounting for coma, shock, and organ failure. Outcome studies examining CA in patients with cardiogenic shock need to account for important differences such as CA rhythm.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".