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A Multifaceted Strategy to Reduce Inappropriate Use of Fresh Frozen Plasma Transfusions in the Intensive Care Unit.

2009· article· en· W2979448310 on OpenAlexaff
Donald M. Arnold, Heather Whittingham, François Lauzier, France Clarke, Ellen McDonald, Andrea Tkaczyk, Angela Greiter, Lily Waugh, Mark Crowther

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversité de SherbrookeSt. Joseph's HospitalMcMaster UniversityCanadian Blood Services
Fundersnot available
KeywordsMedicineFresh frozen plasmaContext (archaeology)Intensive care unitIntensive care medicinePlasmapheresisEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Abstract 1412 Poster Board I-435 Introduction: The overuse of fresh frozen plasma (FFP) transfusions has been well documented, especially among critically ill patients. In a mixed medical surgical intensive care unit (ICU), we documented that 43% of FFP transfusions were given for indications other than those proposed in published guidelines (Lauzier 2007). Methods: We developed a 3-Phase multifaceted behavior-change strategy to curtail inappropriate FFP transfusions, documenting all patients who had FFP, excluding plasmapheresis. Phase I was a 3-month baseline assessment period with no intervention, in which the FFP transfusion orders prescribed at the discretion of the ICU team were recorded. Phase II was a 3-month intervention targeted to all ICU clinicians, comprised of education on the appropriate use of FFP transfusions, audit and feedback of performance indicators, and a pre-order FFP Request Form to specify the indication and the pre-transfusion INR. Phase III was a 9-month assessment period incorporating only the FFP Request Form. At the end of the study, the indications for all transfusions were adjudicated independently in triplicate by 2 ICU clinicians and 1 hematologist, to determine whether each FFP transfusion was a) consistent with published guidelines, b) inconsistent with guidelines but appropriate for the ICU context, or c) inconsistent and inappropriate. Discrepancies were resolved in all cases. FFP orders were not withheld if FFP Request Forms were not completed. Results: Chance-corrected agreement (which considers clustered transfusions within patients) between ICU reviewers on whether transfusions were consistent or appropriate versus inconsistent and inappropriate was high (phi = 0.80). During Phase I (3 months), 66 FFP transfusions were administered (n= 26 patients), of which 30 were for bleeding. During Phase II (3 months), 24 transfusions were administered (n = 11 patients), of which 11 were for bleeding. During Phase III (7 months of data), 96 transfusions were given (n= 41 patients), of which 57 were for bleeding. Rates of FFP transfusions per month for all indications and for bleeding indications were 22 and 10, respectfully in Phase I; 8 and 4 in Phase II; and 14 and 8 in Phase III. A FFP Request form accompanied 39 (40.6%) of 96 FFP transfusions in Phase III. Conclusions: A multifaceted behavior-change strategy appears to be an effective method of changing inappropriate FFP transfusion practices; however satisfactory uptake of a pre-transfusion FFP Request Form requires consistent reminders. We recommend that transfusion guidelines are improved to explicitly incorporate FFP transfusion criteria appropriate for the ICU setting. Disclosures: No relevant conflicts of interest to declare.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.050
GPT teacher head0.275
Teacher spread0.225 · 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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