Individualized transfusion decisions to minimize adverse cardiovascular outcomes in patients with acute myocardial infarction and anemia
Bibliographic record
Abstract
BACKGROUND: Risk-benefit tradeoffs between restrictive versus liberal red blood cell transfusion strategies may vary across individuals. This exploratory analysis aimed to derive and evaluate individualized treatment effects of defined transfusion strategies in patients with acute MI and anemia with the goal of minimizing adverse cardiovascular outcomes. METHODS: This study analyzed 3,447 (98.4%) patients randomized in the MINT (Myocardial Ischemia and Transfusion) trial between April 2017 to April 2023. Outcomes for this analysis included 30-day death or recurrent MI, death, and major adverse cardiovascular events (MACE, a composite of death, MI, stroke, and ischemia-driven unscheduled revascularization). Machine learning methods were used to identify baseline patient characteristics that informed the individualized treatment effect of a restrictive versus liberal transfusion strategy for each patient. The expected population risk of an outcome under a scenario in which patients received their optimal treatment, as indicated by the individualized treatment effect, was contrasted with expected risks for universally applying a restrictive strategy or a liberal strategy to all patients. RESULTS: Baseline characteristics did not inform individualized treatment effects on 30-day death and death or MI, suggesting minimal heterogeneity in treatment effect on these outcomes. An algorithm for estimating the individualized treatment effect on 30-day MACE included 12 baseline factors. If all patients received the optimal treatment as indicated by their estimated individualized treatment effect, the predicted risk of 30-day MACE in the sample population was 15.2% (95% CI 14.2%-16.2%). This corresponded to 4.0 (difference: -4.0%, 95% CI -5.8, -2.1) and 2.3 (difference: -2.3%, 95% CI -3.7, -0.9) percentage point risk reductions compared to applying a restrictive or liberal strategy to everyone respectively. CONCLUSIONS: The MINT trial average treatment effect, favoring a liberal strategy, may be optimal to minimize risk of 30-day death and death or MI for acute MI patients with anemia represented in the MINT sample as no individualized treatment effects were estimated on these outcomes. However, individualized transfusion strategy decisions have potential to reduce risk of 30-day MACE. External validation of the MACE algorithm is required before clinical use. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02981407, https://clinicaltrials.gov/study/NCT02981407.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".