Temporal Trends in in-Hospital Bleeding and Transfusion in a Contemporary Canadian ST-Elevation Myocardial Infarction Patient Population
Bibliographic record
Abstract
Background Although ST-elevation myocardial infarction (STEMI) management has evolved substantially over the past decade, its effect on bleeding and transfusion rates are largely unknown in a contemporary population. Methods Our study cohort included patients 20 years of age or older who were hospitalized for STEMI between 2007 and 2016 across all Canadian provinces, except Quebec. Unadjusted rates of bleeding and of transfusion during STEMI episodes were calculated overall and for each province according to fiscal year. Patients were stratified into 4 groups according to their bleeding/transfusion. Characteristics, treatment, and outcomes were compared between groups. Multivariate logistic regression modelling was used to assess the association between bleeding and transfusion on in-hospital mortality. Results Using 108,832 STEMI episodes, rates of in-hospital bleeding and transfusion declined between 2007 and 2016 from 3.9% to 2.8% ( P < 0.0001) and 4.7% to 3.8% ( P < 0.0001), respectively. However, variation in bleeding and transfusion rates were observed across Canadian provinces. Patients with bleeding or transfusion, were older, female, and had more comorbidities. Compared with patients who did not bleed or receive a transfusion, individuals who bled, were transfused, or bled and were transfused, had higher in-hospital mortality (18.6%, 30.3%, and 30.4%, respectively [ P < 0.0001]). The association remained after adjustment: bleeding (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.8-2.4), transfusion (OR, 4.4; 95% CI, 3.9-4.9), and bleeding and transfusion (OR, 3.8; 95% CI, 3.2-4.6). Conclusions The proportion of Canadian STEMI patients who experienced in-hospital bleeding and transfusion has decreased over the past 9 years. However, patients with bleed or transfusion remain at higher risk of adverse outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".