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The Effect of Platelet Transfusion on Death in the Intensive Care Unit

2016· article· en· W2586292161 on OpenAlexaffabout
Donald M. Arnold, Shuoyan Ning, Rebecca Barty, Yang Liu, Richard J. Cook, Bram Rochberg, Alfonso Iorio, Andrew W. Shih, Nancy M. Heddle

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of WaterlooMcMaster UniversityCanadian Blood Services
Fundersnot available
KeywordsMedicineIntensive care unitPlatelet transfusionMechanical ventilationInternal medicineProportional hazards modelBlood transfusionComplicationPlateletRisk factorSurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background: Thrombocytopenia is a common complication of critical illness and an independent risk factor for death in the intensive care unit (ICU). Whether platelet transfusions modify the risk of death in critically ill patients is unknown. Methods: Adult patients admitted to ICU who received one or more platelet transfusion over a 10-year period (2006 - 2015) from 3 academic hospitals in Canada were analyzed from a blood transfusion registry. Oncology patients were excluded. Contemporaneous non-transfused ICU patients were used as controls. Data from the registry were validated by integrity checks with medical records and laboratory information systems. We estimated the effect of platelet transfusion on mortality in ICU adjusted for baseline and time-varying covariates including multi-organ dysfunction score (MODS) and severity of thrombocytopenia using a stratified cox proportional hazards model. Significance was set at p<0.05 for all analyses. Results: Of 43,234 non-oncology patients admitted to ICU, 5,621 (13.0%) received one or more platelet transfusion. Compared with non-transfused controls, transfused patients had lower platelet counts (median, 82 x109/L vs. 163 x109/L); were more often admitted after surgery (90.7% vs. 46.9%) especially cardiac surgery (86.8% of surgeries vs. 60.6%); and had higher unadjusted mortality (10.7% vs. 6.5%). Using regression analysis adjusted for covariates (nadir platelet count, red blood cell transfusion, need for hemodialysis) and stratified by age, baseline MODS score (available for 66.2% of patients) and need for invasive mechanical ventilation, platelet transfusions were associated with a lower risk of death in ICU [hazard ratio (HR)= 0.66; 95% confidence interval (CI), 0.46 - 0.96; p= 0.028; n= 26,404 with all available data]. A similar effect was observed in the subgroup of cardiac surgery patients (HR= 0.50; 95% CI, 0.26 - 0.98; p=0.044; n= 10,676) but not all surgical patients (HR = 0.73; 95% CI, 0.46 - 1.17; p= 0.188; n= 14,461). Conclusion:After adjusting for illness severity, thrombocytopenia and other confounders common among critically ill patients, platelet transfusions were associated with improved survival in the population of mostly cardiac surgery patients. This potential protective effect of platelet transfusions requires further evaluation in prospective studies. Disclosures Arnold: Novartis: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy; UCB: Consultancy; Amgen: Consultancy, Research Funding.

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)0.001
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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