Red Cell Exchange with a Targeted Ht of 34% in Sickle Cell Disease: A Cohort Study from CHUM
Bibliographic record
Abstract
Background: Approximately 10% of patients with sickle cell disease (SCD) rely on chronic transfusion therapy as part of their management, yet the optimal strategy for regular transfusion programs is not well defined (1). The 2020 ASH guidelines on SCD suggest red cell exchange (RCE) over simple transfusions in patients receiving chronic transfusions, as the latter approach leads to iron overload and risks for end-organ damage, while the former can achieve neutral iron balance (2). Facing a severe blood shortage during the Covid-19 pandemic, caregivers were forced to adopt different blood-saving strategies for SCD patients. Raising the post transfusion targeted hematocrit (Ht) with RCE was suggested as a measure to suppress endogenous erythropoiesis and reduce blood product requirements, and was proven to be effective in a recent single cohort study (3). The investigators proposed it as a temporary measure as red cell mass balance assessments raised some concerns for iron overload. Herein, we report the experience of Centre Hospitalier de l'Université de Montréal (CHUM) with a cohort of patients who have been enrolled in such long-term transfusion strategy. Methods: In this retrospective study conducted at CHUM, all consecutive adult SCD patients were included if they were treated with a chronic RCE program (at least 6 months) between 2019 and 2021 at our institution. The RCE targets were established as follows: post transfusion Ht of 34% regardless of baseline Ht with a remaining fraction of red blood cells of 30%, post transfusion HbS < 30% during the first 2 years and HbS < 50% during the subsequent years for HbSS patients and HbS + HbC < 60% for HbSC patients. Adjustments in RCE frequency were made to achieve HbS/HbC targets. Indications and time between transfusions were collected. The cohort was divided according to post transfusion Ht strategy: increased Ht (IH) vs same Ht (SH). Correlations were made with transfusion requirements and iron status-related outcomes including ferritin balance, prescription of liver MRI and need for chelation. Results: A total of 101 patients were analyzed: 65% HbSS, 27% HbSC and 8% HbS/b+disease. The most common indications for chronic transfusions were progressive cerebral microangiopathy without overt stroke (46%) and recurrent vaso-occlusive crises/acute chest syndromes (30%), and 48% had more than one indication. The median RCE interval was 6.9 weeks [4-10]. Fifty percent of patients had their post transfusion hematocrit increased (IH strategy) compared to 43% with a neutral Ht balance (SH strategy). HbSS patients represented 94% of the IH cohort, with a mean post transfusion Ht increment of 6% [3-11]. HbSC patients (49%) and HbSS (37%) composed a majority of the SH cohort. The IH cohort required less red cell units (RCU) per year compared to the SH cohort (65 vs 80 RCU per year on average, p=0.0004). This finding remained consistent when correcting for patient weight and baseline hematocrit using red cell volume estimation (RCV), with a lower transfused red cell volume per year in the IH cohort (6.8 vs 8.1 approximated RCV per year, p=0.01). There was also a trend for longer time between RCE procedures in the IH cohort (50 vs 47 days, p=0.08). A significantly higher number of patients in the IH group reached a ferritin value > 1000 ng/mL (65% vs 5%, p<0.001) and 51% of IH vs 0% of SH patients were started on iron chelation therapy (p<0.001). Median time to chelation initiation was 19.1 months [5.5-33.2 months]. Conclusion: In our center, with a fixed post transfusion hematocrit of 34%, raising hematocrit > 2% above pre transfusion level resulted in iron overload in 65% of patients and prescription of chelation in half of patients. Our results suggest that hematocrit balance before and after RCE can be used to predict net iron balance in patients on chronic transfusion therapy. This strategy should be formally compared with an isoHt strategy in patients with a comparable baseline hematocrit to assess iron overload, annual blood product requirements, resource utilization and patient quality of life. References 1.Kelly S, et al.,Transfusion. 2020;60(11):2508-16. 2.Chou ST et al., Blood Advances. 2020;4(2):327-55. 3.Uter S et al., Blood Advances. 2021;5(12):2586-92.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".