A Health Technology Reassessment of Red Blood Cell Transfusions in the Intensive Care Unit
Bibliographic record
Abstract
Health technology reassessment (HTR) is the systematic, evidence-based assessment of the clinical, economic, ethical, and social impacts of an existing health technology to inform its optimal use. A novel model to guide HTR processes has been proposed. The overall aim of this thesis was to test the HTR model in a real-world healthcare context with the technology of red blood cell (RBC) transfusions in the intensive care unit (ICU). This thesis is comprised of 4 studies. The first study was a retrospective observational study of RBC transfusions in 9 Alberta ICUs. Between April 1, 2014 and December 31, 2016, we found that 61% of included RBC transfusions in stable, non-bleeding ICU patients were associated with a pre-transfusion hemoglobin value of 70 g/L or more and cost an estimated $1.82M in healthcare costs. Second, we conducted a systematic review and meta-analyses to determine the effectiveness of interventions on healthcare providers’ RBC transfusion practices. We identified a large and heterogenous body of evidence. Use of any intervention was associated with reduced odds of transfusion, including inappropriate transfusions. However, there was limited understanding of why interventions were selected over others and how this may have affected outcomes. In the third study, we conducted a population-based cross-sectional survey of Alberta ICU physicians to understand their perceptions of RBC transfusions practices using the Theoretical Domains Framework. We identified self-reported facilitators and barriers to practicing a guideline-recommended restrictive RBC transfusion strategy, which could then be mapped to relevant behaviour change interventions to optimize RBC transfusions. Finally, we conducted a controlled before and after pilot study to assess the feasibility of implementing a multi-modal intervention to optimize RBC transfusions in the ICU. The intervention was theory-informed and co-designed with local clinical leaders and included group education and audit and feedback. Early and meaningful stakeholder engagement and tailoring the intervention to interdisciplinary healthcare providers were important for achieving feasibility. Overall, we uncovered critical methodological and practical considerations to advance the emerging field of HTR. With regards to optimizing RBC transfusions, we established the necessary foundation to implement, monitor, and evaluate a larger-scale HTR initiative for ICUs in Alberta.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".