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Restrictive versus liberal blood transfusion strategies in patients with myocardial infarction and anemia: individual patient data meta-analysis of randomized trials

2024· article· en· W4403847500 on OpenAlexaff
Jeffrey Carson, Dean Fergusson, Helaine Noveck, Ranjeeta Mallick, Sunil V. Rao, Tabassome Simon, Philippe Gabríel Steg, Howard A. Cooper, Shaun G. Goodman, John H. Alexander, Simon Stanworth, Maria M. Brooks, Paul Hébert

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsBruyèreUniversity of OttawaUniversity of TorontoOttawa Hospital
Fundersnot available
KeywordsMedicineMeta-analysisRandomized controlled trialMyocardial infarctionAnemiaBlood transfusionInternal medicineCardiologyIntensive care medicine

Abstract

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Abstract Background There are insufficient data to guide the appropriate use of red blood cell transfusion in patients with anemia and acute myocardial infarction (MI). Purpose To combine individual patient data from randomized clinical trials evaluating restrictive versus liberal transfusion strategies in patients with acute MI and anemia to generate precise estimates of treatment effects. Methods We searched all major databases to identify trials comparing restrictive versus liberal transfusion strategies in patients with acute MI and anemia (Hgb < 10 g/dl). We included studies that allocated participants to a liberal transfusion strategy that maintained hemoglobin greater than 10 g/dL or a restrictive transfusion strategy in which transfusion was administered if hemoglobin concentration was less than 8 g/dL. The primary outcome was a composite of 30-day death or MI. Secondary outcomes included 30-day death, 30-day cardiac death, and 6-month death. One-stage individual patient data meta-analyses were performed using a multilevel generalized linear model accounting for the clustering of patients within trials. Results We included 4311 participants from four trials. The primary outcome occurred in 334 patients (15.4%) in the restrictive and 296 patients (13.8%) in the liberal strategy (relative risk (RR) 1.13, 95% confidence interval (CI), 0.97 to 1.30) (figure 1). Death at 30-days occurred in 201 patients (9.3%) in the restrictive and 174 patients (8.1%) in the liberal strategy (RR 1.15; 95% CI, 0.95 to 1.39). Cardiac death at 30 days occurred in 5.5% in the restrictive strategy compared to 3.7% in the liberal strategy (RR 1.47; 95% CI, 1.11 to 1.94). Heart failure and thromboembolism occurred slightly more frequently in the liberal transfusion strategy group. Death at six months was higher in the restrictive (20.5%) compared to the liberal strategy (19.1%) (hazard ratio (HR) 1.08; 95% CI 1.05 to 1.11) (figure 2). The HR for cardiac death at 6 months was higher with a restrictive than liberal strategy (HR 1.38; 95% CI 1.08 to 1.76). In contrast, the HR for non-cardiac death at 6 months was lower with a restrictive than a liberal transfusion strategy (HR 0.89; 95% CI 0.84 to 0.94). Adjustment for baseline characteristics minimally changed the relative risk. Results for subgroups were similar except in patients without a history of renal failure whose risk of death or MI was increased with a restrictive strategy (RR 1.19; 95% CI, 1.01 to 1.39). Conclusions Pooled data from randomized trials found no significant differences for the primary outcome 30-day death or MI, but risk of cardiac death at 30 days and all cause death at 6 months was higher with a restrictive transfusion strategy. Given most outcomes favored liberal transfusion, and the risks from liberal transfusion were low, it may be prudent to use a liberal transfusion strategy in patients with acute 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)0.033
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.066
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0240.048
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.131
GPT teacher head0.349
Teacher spread0.218 · 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 designMeta-analysis
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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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