Location of death among patients presenting with cardiovascular disease to the emergency department in the United states
Bibliographic record
Abstract
BACKGROUND: In-hospital deaths are an important outcome and little is known about deaths in the emergency department (ED). Among patients who died of cardiovascular diseases (CVD), we assessed causes of death, temporal trends and the relative distribution of deaths in the ED versus hospital. METHODS: Using the United States Nationwide Emergency Department Sample, we conducted a retrospective study of patients presenting to the ED with a primary diagnosis of CVD between 2006 and 2014. We used descriptive statistics to describe causes of deaths, temporal trends and location of death. RESULTS: During the study period, there were 27 144 508 visits to the ED with CVD diagnoses (~2% of all ED visits,). The most common CVD diagnoses were heart failure (n = 8 571 598), acute myocardial infarction (n = 4 827 518) and atrial fibrillation/flutter (n = 4 713 241). There were a total of 2.2 million deaths caused by the CVD, with the majority (57.6%) occurring in the ED. Cardiac arrest was the most common cause of in-hospital death (n = 1 225 095, 55.3%), followed by acute myocardial infarction (n = 279 310, 12.6%), heart failure (n = 217 367, 9.8%), intracranial hemorrhage (n = 168 009, 7.6%) and ischemic stroke (n = 151 615, 6.8%). The proportion of deaths in the ED for these causes were 91.9% cardiac arrest (n = 1 173 471), 3.6% acute myocardial infarction (n = 46 909), 1.0% heart failure (n = 12 599) and 1.1% intracranial hemorrhage (n = 13 579). There was a decrease in death for most CVDs over time. CONCLUSIONS: Inpatient CVD admissions and their associated death may not be a robust measure of the national burden of CVD since ED death-which are common for some conditions-are not captured.
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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 | 0.004 | 0.022 |
| 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".