A Feasibility Randomized Trial of Red Cell Transfusion Thresholds in Myelodysplasia
Bibliographic record
Abstract
Abstract Background Red cell transfusion is the most common intervention in the management of myelodysplastic syndromes (MDS). Transfusion practice in MDS is typically characterized by transfusion of multiple (usually 2 - 4) units every 3 - 4 weeks, but there is no supporting evidence for efficacy of this strategy. The aim of this trial was to explore the feasibility of (and adherence to) two standardized transfusion algorithms (liberal vs. restrictive), in order to inform future research. This trial is aligned with a Canadian study (NCT02099669). Methods The study was undertaken in the UK and Australia/New Zealand (ISRCTN26088319). Inclusion criteria were MDS patients with <20% bone marrow blasts, who were transfusion dependent (at least 1 red cell transfusion episode per month in the last 8 weeks) and receiving no additional MDS therapy. Randomization was to one of two red cell transfusion strategies over a 12-week period after a 4-week run-in to achieve a Hb >100 g/l prior to initiation of the allocated transfusion arm: (1) Restrictive (control arm) transfusion strategy to maintain hemoglobin concentration (Hb) between 85 and 100g/L; 2 units of packed red cell unit transfusions were transfused when Hb was <80g/L and 1 unit of packed red cell unit transfusions when Hb is 80-85g/L. (2) Liberal transfusion strategy to maintain Hb between 110 and 125g/L; 2 units of packed red cell unit transfusions were transfused when Hb was <105g/L and 1 unit of packed red cell unit transfusions when Hb is 105-110g/L. Primary outcomes were: percentage of pre-transfusion Hb concentrations below the target range of the assigned strategy, and achievement of at least a 20g/L difference between the mean pre-transfusion Hb in the two transfusion groups. It was pre-specified that the study would be considered feasible if compliance to the pre-transfusion target Hb was ≥70%, with evidence of a difference in Hb between the two arms. Secondary outcomes included quality of life questionnaires (EQ-5D-5L and EORTC QLQ-C30). The primary analysis was intention to treat and these results are reported. Results 38 patients were randomized from 12 hospitals (n=20, restrictive; n=18 liberal). Median age was 79 years (IQR 69-82). Although generally well matched, there were some minor imbalances in baseline characteristics (ECOG status, WHO subtype, prior iron chelation treatment and heart failure medication). Thirty-four participants received at least one transfusion during the trial and were included in the 'compliance to treatment threshold' analysis. Percentage compliance (95% confidence interval) of pre-transfusion Hb being below the target range of the RBC transfusion threshold assigned were 86% (75-94) and 99% (95-100) for the restrictive and liberal arms respectively. As compliance was ≥70% in both arms, the study was declared feasible. The mean (standard deviation (SD)) pre-transfusion Hb for the restrictive and liberal arms were 80 (6) g/l and 97 (7) g/l respectively, for a significant difference between the two groups (p<0.0001). Figure 1 shows the mean (±SD) Hb by week for all randomized participants by treatment arm. The total number of red cell units transfused was 82 in the restrictive group and 192 in the liberal group. Of 8 serious adverse events reported, 1 was deemed potentially related to transfusion. Compliance for completion of EQ-5D-5L and EORTC QLQ-C30 questionnaires was good (between 70 - 80%). Although interpretation of quality of life analyses is exploratory, the numbers of participants achieving a (pre-defined) clinically meaningful increase showed small improvements favoring the liberal policy across the following domains (EQ-5D-5L descriptive; EORTC QLQ-C30: physical functioning and global health score). Discussion These results of this multicenter trial in an older out-patient based population support the feasibility of progressing to a definitive trial of different red cell transfusion strategies, in order to evaluate the comparative effectiveness and safety for clinically relevant outcomes of a restrictive vs liberal transfusion policy for transfusion-dependent MDS. The large difference in units transfused between the two arms challenges perceived dogma and supports the need for a cost-effectiveness component in follow-on trials. (Funding: NHSBT, ANZSBT). Disclosures Buckstein: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".