Blood Transfusion in Patients With Acute Myocardial Infarction, Anemia, and Heart Failure: Lessons From MINT
Bibliographic record
Abstract
BACKGROUND: Blood transfusion may precipitate adverse outcomes, including heart failure (HF), among patients with acute myocardial infarction (MI). This study characterizes the effects of a restrictive or liberal transfusion strategy on outcomes in patients with MI and anemia with and without baseline HF. METHODS: In the MINT trial (Myocardial Ischemia and Transfusion), 3504 patients with MI and anemia (hemoglobin <10 g/dL) were randomized to a restrictive (hemoglobin <8 g/dL) or liberal (hemoglobin <10 g/dL) transfusion strategy. We compared the effects of transfusion strategy on outcomes among patients with and without baseline HF. The primary outcome was death or HF at 30 days. RESULTS: Compared with patients without baseline HF (n=1633), those with baseline HF (n=1871) had higher rates of death or HF (18.0% versus 10.0%) at 30 days. Restrictive transfusion resulted in numerically higher rates of death or HF (rate ratio, 1.20 [95% CI, 0.99–1.45] versus 0.94 [95% CI, 0.70–1.26]; P interaction =0.18) in patients with than in those without baseline HF. Among secondary outcomes, death or recurrent MI and death were more frequent among those with baseline HF. Restrictive transfusion resulted in numerically higher rates of death or MI and death in patients with than in those without baseline HF. Rates of HF were similar between restrictive and liberal transfusion in patients with baseline HF but lower with restrictive transfusion (rate ratio, 0.51 [95% CI, 0.29–0.92]; P interaction =0.02) in patients without baseline HF. CONCLUSIONS: A liberal transfusion strategy is safe for patients with MI and anemia, including those with baseline HF. Restrictive transfusion tended to result in worse outcomes, particularly in patients with baseline HF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02981407.
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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 | 0.000 | 0.000 |
| 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".