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Record W2534404687 · doi:10.1056/nejmoa1609014

Effect of Short-Term vs. Long-Term Blood Storage on Mortality after Transfusion

2016· article· en· W2534404687 on OpenAlexaffabout
Nancy M. Heddle, Richard J. Cook, Donald M. Arnold, Yang Liu, Rebecca Barty, Mark Crowther, P.J. Devereaux, Jack Hirsh, Theodore E. Warkentin, Kathryn E. Webert, David Roxby, Magdalena Sobieraj‐Teague, Andrea Kurz, Daniel I. Sessler, Priscilla Figueroa, Martin Ellis, John W. Eikelboom

Bibliographic record

VenueNew England Journal of Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsThrombosis and Atherosclerosis Research InstitutePopulation Health Research InstituteUniversity of WaterlooMcMaster UniversityCanadian Blood Services
Fundersnot available
KeywordsMedicineRandomizationBlood transfusionRandomized controlled trialPopulationLogistic regressionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Randomized, controlled trials have suggested that the transfusion of blood after prolonged storage does not increase the risk of adverse outcomes among patients, although most of these trials were restricted to high-risk populations and were not powered to detect small but clinically important differences in mortality. We sought to find out whether the duration of blood storage would have an effect on mortality after transfusion in a general population of hospitalized patients. METHODS: In this pragmatic, randomized, controlled trial conducted at six hospitals in four countries, we randomly assigned patients who required a red-cell transfusion to receive blood that had been stored for the shortest duration (short-term storage group) or the longest duration (long-term storage group) in a 1:2 ratio. Only patients with type A or O blood were included in the primary analysis, since pilot data suggested that our goal of achieving a difference in the mean duration of blood storage of at least 10 days would not be possible with other blood types. Written informed consent was waived because all the patients received treatment consistent with the current standard of care. The primary outcome was in-hospital mortality, which was estimated by means of a logistic-regression model after adjustment for study center and patient blood type. RESULTS: From April 2012 through October 2015, a total of 31,497 patients underwent randomization. Of these patients, 6761 who did not meet all the enrollment criteria were excluded after randomization. The primary analysis included 20,858 patients with type A or O blood. Of these patients, 6936 were assigned to the short-term storage group and 13,922 to the long-term storage group. The mean storage duration was 13.0 days in the short-term storage group and 23.6 days in the long-term storage group. There were 634 deaths (9.1%) in the short-term storage group and 1213 (8.7%) in the long-term storage group (odds ratio, 1.05; 95% confidence interval [CI], 0.95 to 1.16; P=0.34). When the analysis was expanded to include the 24,736 patients with any blood type, the results were similar, with rates of death of 9.1% and 8.8%, respectively (odds ratio, 1.04; 95% CI, 0.95 to 1.14; P=0.38). Additional results were consistent in three prespecified high-risk subgroups (patients undergoing cardiovascular surgery, those admitted to intensive care, and those with cancer). CONCLUSIONS: Among patients in a general hospital population, there was no significant difference in the rate of death among those who underwent transfusion with the freshest available blood and those who underwent transfusion according to the standard practice of transfusing the oldest available blood. (Funded by the Canadian Institutes of Health Research and others; INFORM Current Controlled Trials number, ISRCTN08118744 .).

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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 designObservational
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

Citations325
Published2016
Admission routes2
Has abstractyes

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