Pre-Hospital Cardiac Arrest in Acute Coronary Syndromes: Insights from the Global Registry of Acute Coronary Events and the Canadian Registry of Acute Coronary Events
Bibliographic record
Abstract
OBJECTIVES: Cardiac arrest in acute coronary syndromes (ACS) is associated with high morbidity and mortality. We examined the clinical characteristics, contemporary management patterns and outcomes of ACS patients with pre-hospital cardiac arrest. METHODS: The Global Registry of Acute Coronary Events and the Canadian Registry of Acute Coronary Events enrolled 14,010 ACS patients in 1999-2008. We compared the clinical characteristics, in-hospital treatment and outcomes between patients with and without pre-hospital cardiac arrest. RESULTS: Overall, 206 (1.4%) patients had cardiac arrest prior to hospital presentation. ACS patients with pre-hospital cardiac arrest were less frequently treated with aspirin, β-blocker, angiotensin-converting enzyme inhibitors, and statins within the first 24 h of presentation, but the use of cardiac procedures was similar compared to the group without cardiac arrest. Patients with pre-hospital cardiac arrest had significantly higher rates of in-hospital adverse events. Factors independently associated with pre-hospital cardiac arrest included male gender, current smoker status, tachycardia, higher Killip class and ST-segment deviation. CONCLUSION: ACS patients with pre-hospital cardiac arrest continue to have more in-hospital complications and higher mortality. Their use of evidence-based medical therapies was lower but the use of cardiac procedures was similar compared to the group without cardiac arrest. Better utilization of evidence-based therapies in these patients may translate into improved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".