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 last decade, its impact on bleeding and transfusion rates are largely unknown in a ‘real-world’ Canadian population. Using a large Canadian population health database, we evaluated the temporal trends of in-hospital bleeding and transfusion rates in a contemporary Canadian STEMI population. Methods: Data from the Canadian Institute for Health Information (CIHI) included patients 20 years of age hospitalized for STEMI between April 2007 and March 2016 across all Canadian hospitals, except Quebec. Patients who underwent coronary artery bypass grafting (CABG) during hospitalization were excluded. International Classification of Diseases-10th Revision codes were used to determine STEMI episodes, identify in-hospital bleeding events, and comorbidities. The CIHI database also contained details on patients receiving transfusion during hospitalizations. Associations between bleeding or transfusion and in-hospital death or 30-day readmission with bleeding or transfusion were also reported. Results: Using 115,078 STEMI episodes, rates of in-hospital bleeding and transfusion declined between 2007 and 2016 from 4.0% 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 who experienced bleeding, or received a transfusion, were older, female, and more commonly had diabetes, hypertension, or heart failure. Compared to patients who did not bleed or receive a transfusion, individuals who bled, or those who were transfused, or those who bled and were transfused, had increased median length of stay (7 days, 11 days, and 14 days, respectively [p<0.0001]). These groups also had higher in-hospital mortality (19.4%, 31.1%, and 30.7%, respectively [p<0.0001]). Conclusion In summary, the proportion of Canadian STEMI patients experiencing in-hospital bleeding and transfusion have decreased over the past nine years. Further exploration is required to identify the practice patterns used to protect patients from bleeding events and excessive transfusions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".