Abstract 20678: A Systematic Review of World-Wide Characteristics and Management of Patients With ST-Segment Elevation Myocardial Infarction
Bibliographic record
Abstract
Background: Management of ST elevation myocardial infarction (STEMI) has made tremendous progresses during the last decades. However, it remains uncertain whether all STEMI patients are receiving optimal care and whether variation in care has any impact on their outcomes. We aim to characterize the contemporary global characteristics, managements, and outcomes of STEMI patients. Methods and Results: We searched EMBASE/MedLINE Ovid for observational data of patients with STEMI. We identified 17 studies enrolling 112 772 patients in 20 countries during the last 5 years (2008-2013). The median age ranged from 54 to 66 years with 13%-33% females. Twelve percent to 39% of patients presented in Killip heart failure class 2-4. In-hospital use of aspirin (ASA), P2Y12 inhibitor/thienopyridines, and systemic anticoagulation was 90-99%, 77-97%, and 61-100% respectively. Reperfusion was provided for 63%-97% of patients. Fibrinolysis was used in 0.7%-66% with a door-to-needle (D2N) time of 28-65 minutes; 12%-74% with D2N <30minutes. Primary percutaneous coronary intervention was performed for 17%-97% with a door-to-balloon (D2B) time of 40-125 minutes; 40%-94% had D2B <90 minutes. Emergency cardiac surgery was performed in 0.4%-8% of patients. Discharge prescriptions included ASA, thienopyridines/P2Y12 inhibitors, beta-blockers, and statins in 85%-99%, 77%-97%, 54%-83%, and 64%-95% respectively. In-hospital outcomes included death (2%-10%), recurrent myocardial infarction (0.4%-5%), stroke (0.2%-1.6%), major bleeding (0.3%-7%). The median hospital stay ranged from 4-6 days. Conclusion: Despite recent progresses in STEMI care, there remains marked heterogeneity in STEMI care and outcomes worldwide that warrants further attention. Identification of gaps to STEMI care and remedial actions may improve the global outcomes of STEMI patients.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".