Utilising red cell antigen genotyping and serological phenotyping in sickle cell disease patients to risk‐stratify patients for alloimmunisation risk
Bibliographic record
Abstract
BACKGROUND: Alloimmunisation and haemolytic transfusion reactions (HTRs) can occur in patients with sickle cell disease (SCD) despite providing phenotype-matched red blood cell (RBC) transfusions. Variant RBC antigen gene alleles/polymorphisms can lead to discrepancies in serological phenotyping. We evaluated differences between RBC antigen genotyping and phenotyping methods and retrospectively assessed if partial antigen expression may lead to increased risk of alloimmunisation and HTRs in SCD patients at a tertiary centre in Canada. METHODS: RBC antigen phenotyping and genotyping were performed by a reference laboratory on consenting SCD patients. Patient demographic, clinical and transfusion-related data were obtained from a local transfusion registry and chart review after research ethics board approval. RESULTS: A total of 106 SCD patients were enrolled, and 91% (n = 96) showed additional clinically relevant genotyping information when compared to serological phenotyping alone. FY*02N.01 (FY*B GATA-1) (n = 95; 90%) and RH variant alleles (n = 52, 49%; majority accompanied by FY*02N.01) were common, the latter with putative partial antigen expression in 25 patients. Variability in genotype-phenotype antigen prediction occurred mostly in the Rh system, notably with the e antigen (kappa: 0.17). Fifteen (14.2%) patients had a history of alloimmunisation, with five having HTR documented; no differences in clinical outcomes were found in patients with partial antigen expression. Genotype/extended-phenotype matching strategies may have prevented alloimmunisation events. CONCLUSION: We show a high frequency of variant alleles/polymorphisms in the SCD population, where genotyping may complement serological phenotyping. Genotyping SCD patients before transfusion may prevent alloimmunisation and HTRs, and knowledge of the FY*02N.01 variant allele increases feasibility of finding compatible blood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".