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Effects of restrictive vs liberal transfusion strategies on mortality in patients with acute myocardial infarction and anemia: The 6-month follow up primary results of the MINT trial

2024· article· en· W4403806749 on OpenAlexaff
Tabassome Simon, Hutton Brandon, Maria M. Brooks, Shaun Goodman, John H. Alexander, Philippe Gabríel Steg, Renato D. Lópes, Shahab Ghafghazi, Claire Bouleti, Howard Cooper, Eric McCamant, Herbert D. Aronow, Paul C. Hébert, Jeffrey L. Carson

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMyocardial infarctionAnemiaCardiologyInternal medicineIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

Abstract Background The Myocardial Ischemia and Transfusion (MINT) trial identified a statistically non-significant increase in risk of 30-day death or myocardial infarction (MI) (primary endpoint) and all-cause death and a statistically significant increase in cardiac death in patients with an acute MI and anemia assigned to a restrictive transfusion strategy compared to a liberal transfusion strategy. Purpose To determine the effect of a restrictive versus a liberal transfusion strategy on all-cause and cause-specific death 6-months post-randomization (secondary endpoint). Methods The MINT trial randomized 3,504 patients with MI and hemoglobin <10 g/dL to a restrictive (hemoglobin threshold of 7 or 8 g/dL) or liberal (hemoglobin threshold of <10 g/dL) transfusion strategy implemented during the index hospitalization. Sites contacted patients at 30-days and 6-months post-randomization and recorded patient vital status, date of death, and site-reported cause of death (categorized as cardiac, non-cardiac, or unknown). A competing risk analysis (cumulative incidence estimation and Fine-Gray models) was conducted to estimate the effect of transfusion strategy on all-cause and cause-specific death. Results Patients were enrolled from 6 countries between 2017 and 2023; average age was 72 years, and 46% were female. Vital status at 6-months was obtained for 3419 (97.6%) randomized patients (restrictive: 98.1%; liberal: 97.0%); 2,690 (76.8%) patients were alive and 729 (20.8%) had died. At 6-months, all-cause death occurred in 376 patients in the restrictive arm (21.7%) and 353 in the liberal arm (20.5%), hazard ratio [HR]: 1.07, 95% CI: 0.93–1.24. Compared to the liberal arm, a greater proportion of deaths in the restrictive arm were cardiac (41.8% vs. 29.8%). The risk of cardiac death was significantly higher in patients in the restrictive arm (9.0% vs. 6.1%, HR: 1.52; 95% CI: 1.19–1.94) whereas risk of non-cardiac death (HR: 0.87; 95% CI: 0.70–1.08) and unknown cause of death (HR: 0.88; 95% CI: 0.64–1.21) did not differ between the restrictive and the liberal arms. Conclusions In patients with acute MI and anemia, the 6-month incidence of all-cause death did not differ with a restrictive versus a liberal transfusion strategy. However, the numerically higher rate of all-cause mortality, seen with the restrictive strategy at 30-days, persisted through 6-months and was driven by cardiac causes. The long-term findings from the MINT trial suggest that a restrictive transfusion strategy may be harmful for patients with MI and anemia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.289
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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