ONTraC: A 20-Year History of a Successfully Coordinated Provincewide Patient Blood Management Program: Lessons Learned and Goals Achieved
Bibliographic record
Abstract
Our understanding of the risks associated with perioperative anemia and transfusion, in terms of increased morbidity and mortality, has evolved over the past 2 decades. By contrast, our understanding of the potential mechanisms of injury and optimal treatment strategies remains incomplete. As such, the important role of effective patient blood management (PBM) programs, which address both the effective treatment of anemia and minimizes the need for red blood cell (RBC) transfusion, is of central importance to optimizing patient care and improving patient outcomes. We report on important clinical outcomes of the Ontario Transfusion Coordinator (ONTraC Program), a network of 25 hospital sites, working in coordination over the past 20 years. Transfusion nurse coordinators were assigned to apply multimodal best practice in PBM (including recommended changes in surgical approach; diagnosis, assessment, and treatment of anemia; and adherence to more restrictive RBC transfusion thresholds). Data were collected on various clinical parameters. We further described lessons learned and difficulties encountered in this multisite PBM initiative. A significant reduction in RBC transfusions was observed for numerous indexed surgeries. For example, RBC transfusion rates for knee arthroplasty decreased from 25% in 2002 to 0.4% in 2020. For coronary artery bypass graft (CABG) surgery, transfusion rates decreased from 60% in 2002 to 27% in 2020. We also observed a decrease in RBC units utilized per transfused patient for knee (2.1 ± 0.5 [2002] vs 1.0 ± 0.6 [2020] units per patient) and CABG surgery (3.3 ± 0.6 [2002] vs 2.3 ± 1.9 [2020] units per patient). These reductions were associated with favorable clinical outcomes, including reduced length of hospital stay (P = .00003) and a reduced rate of perioperative infections (P < .001) for nontransfused versus transfused patients. These advances have been achieved with estimated savings in the tens of millions of dollars annually. Our experience and data support the hypothesis that instituting an integrated network of transfusion nurse coordinators can provide an effective provincewide PBM program, reduce RBC transfusions, improve some patient outcomes, and reduce health care costs, as an example of a "win-win-win" medical program.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".