Comparison of Prophylactic and Therapeutic Platelet Transfusion Strategies in Manitoba: A Retrospective Cohort Study
Bibliographic record
Abstract
Background:Autologous bone marrow transplantation (BMT) is associated with severe thrombocytopenia, yet optimal use of platelet transfusions is unclear. Subgroup analyses from prior trials1,2 demonstrated no difference in major bleeding among patients treated with prophylactic (to prevent bleeding) or therapeutic (to treat bleeding) platelet transfusion strategies, although total blood product use was reduced when a therapeutic approach was used. Guidelines suggest avoiding prophylactic platelet transfusions in autologous BMT in the absence of bleeding3,4, yet there has been variable uptake of a therapeutic platelet transfusion strategy among centres performing autologous BMT. In 2018 we adopted a policy of therapeutic rather than prophylactic platelet transfusion among patients undergoing autologous BMT; our prior policy was to prophylactically transfuse when the platelet count fell below 10x109/L. We compared bleeding outcomes and overall blood product utilization among patients undergoing autologous BMT treated before and after our change in platelet transfusion policy. Methods:We conducted a retrospective cohort study of all adult patients undergoing autologous BMT for hematologic malignancy in Manitoba, CAN from 2013-2022. Patients were identified, and demographic and clinical information were obtained from the Manitoba Blood and Marrow Transplant Program registry. Hospital records were reviewed to assess bleeding frequency and severity using the modified World Health Organization bleeding scale5. Transfusion data were obtained from the provincial transfusion database. Transplants conducted before and after June 2018 defined our ‘pre’ and ‘post’ cohort, respectively. Baseline characteristics were summarized as means (standard deviation [SD]), medians (interquartile range [IQR]) or frequency (percent), as appropriate. Logistic regression was performed to assess differences in WHO grade 2+ bleeding, controlling for age, sex, comorbidity score, diagnosis, conditioning regimen, number of days with platelet count <10x109/L, proportion of ‘at risk days’ (days with platelet count <10x109/L) on which platelet transfusion was received, and pre/post prophylactic platelet transfusion protocol implementation status. Results:Of the 320 patients who underwent autologous BMT, 172 (54%) and 148 (46%) patients were in the pre- and post-cohort, respectively. The mean overall age was 56 (SD 12) years, and 60% were male. Lymphoma (58%, n=187) and plasma cell dyscrasias (41%, n=132) were the most common indications for transplant. The median duration of hospitalization was 19 days (IQR 17, 23) in the pre-cohort compared to 23 days (IQR 20, 29) in the post-cohort. The mean number of platelet transfusions per hospitalization was 2.8 (SD 4.2) units pre- and 2.5 (2.7) units post- policy change. The mean number of red blood cell transfusions per hospitalization was 3.4 (SD 4.4) units pre- and 2.7 (SD 2.2) units post- policy change. The mean number of days with a platelet count <10x109/L was 1 (SD 1.5) day in the pre- and 3.5 (SD 3) days in the post-cohort. Bleeding was more common post- policy change compared to pre- policy change (70% vs. 42%; p<0.01). Most bleeding events in the pre-cohort were grade 1 (27%; n=20) or grade 2 (68%; n=50); only one patient experienced a grade 3 bleed. Grade 1 bleeds included epistaxis (55%), oropharyngeal (25%), and petechiae (20%). The most common grade 2 bleeds were PICC-associated (66%) and gastrointestinal (20%). Most bleeding events in the post- cohort were also grade 1 (41%; n=43) or grade 2 (58%); only one patient experienced a grade 3 bleed. The grade 1 bleeds included epistaxis (53%), oropharyngeal (19%) and petechiae (12%). The most common grade 2 bleeds were PICC-associated (40%) and gastrointestinal (22%). The average number of days with bleeding per hospitalization was 1.5 (SD 1.3) days and 1.5 (SD 1.5) days in the pre- and post-cohorts, respectively. On multivariate analysis, post-policy change was not associated with an increase in grade 2+ bleeding (OR 1.43, 95% CI 0.72, 2.84). Conclusion: A therapeutic platelet transfusion strategy among patients undergoing autologous BMT for hematologic malignancy was associated with increased grade 1 and 2 bleeding, with minimal impact on platelet product utilization. Grade 3 or more bleeding events were uncommon, regardless of the platelet transfusion strategy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".