Creating and evaluating a decision support tool to reduce critically low hemoglobin and blood transfusions in-hospital
Bibliographic record
Abstract
ABSTRACT Background Hospitalized patients have multiple risk factors for bleeding including critical illness, interventional procedures, frequent phlebotomy, and use of anticoagulant and antiplatelet medications. It is unknown if a decision support tool to identify inpatients at highest risk of bleeding can prevent critically low hemoglobin and its associated sequelae, such as requiring blood transfusion. Objective To create a decision support tool to identify hospitalized patients at high risk of bleeding sequelae (“high risk”) and to evaluate the impact of this tool on rates of critically low hemoglobin and blood transfusion. Methods We conducted a cohort study of patients hospitalized under general internal medicine at a tertiary-care teaching hospital in Toronto, Ontario. We defined “high risk” as patients with a hemoglobin < 86 g/L, or an absolute decrease in hemoglobin of ≥ 30 g/L, or a platelet count < 50 x 10 9 /L. We then created, implemented, and prospectively evaluated a decision support tool to identify these high-risk patients and alert their clinical team. Our primary outcome was the percentage of patients who received a blood transfusion. Results Our retrospective pre-deployment phase included 6,401 hospitalizations and our prospective phase included 4,274 hospitalizations. The median age of patients was 67 years (IQR 52,80), 43% were female, and the median length of stay was 5 days (IQR 2,10). Overall, 9% had a hemoglobin of 70 g/L or lower and 10% received a blood transfusion in hospital. After model implementation, the median timing of alerts was 1 day after admission (IQR 1,4). The most common trigger for an alert was a hemoglobin < 86 g/L (N=624, 76%), followed by a decrease in hemoglobin ≥ 30 g/L (N=131, 16%), and a platelet count < 50 x 10 9 /L (N=37, 5%). Deployment of the decision support tool was associated with a reduction in the primary outcome (OR 0.78, 95% CI 0.64, 0.96), which was driven by a reduction in red blood cell transfusion (OR 0.78, 95% CI 0.64, 0.96) as opposed to platelet transfusion (OR 1.10, 95% CI 0.46, 2.91). We also observed a reduction in our secondary outcome of critically low hemoglobin (OR 0.81, 95% CI 0.65, 0.99). Interpretation Our decision support tool was associated with a modest reduction in our primary outcome of blood transfusions and secondary outcome of critically low hemoglobin. Because our study was single-centre, we believe future larger studies are needed to validate our findings.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".