A sensitive and rapid alternative to HLA typing as a genetic screening test for abacavir hypersensitivity syndrome
Bibliographic record
Abstract
BACKGROUND: Abacavir hypersensitivity reaction (ABC HSR) is a potentially life-threatening adverse reaction that affects approximately 8% of patients that initiate this antiretroviral drug. Independent groups have shown a strong predictive association between ABC HSR and HLA-B*5701, indicating that exclusion of HLA-B*5701 positive individuals from abacavir treatment would largely prevent ABC HSR. However, the limited availability and relatively high cost of human leukocyte antigen (HLA) typing represent barriers to the widespread implementation of this pharmacogenetic approach to abacavir prescribing. To facilitate routine screening, we have developed a rapid flow cytometry method for HLA-B57 phenotyping using commercially available B17 monoclonal antibodies. METHODS: Whole blood samples from 84 human immunodeficiency virus (HIV) patients were examined by standard flow cytometry methods, using a two-colour B17-specific immunofluorescence assay in the CD45 lymphocyte population. RESULTS: All eight HLA-B57 individuals examined tested positive, while HLA-B57/58 negative individuals (n=74) tested negative for this flow cytometry test. Two non-HLA-B57 individuals showed weak cross-reactivity. CONCLUSION: In our predominantly Caucasian population, B17/CD45 dual staining was sufficient to identify individuals carrying B17 cell surface antigens. This approach, utilizing flow cytometry methods that are widely available in HIV laboratories, therefore offers a sensitive, rapid and cost-effective screening assay prior to abacavir prescription. Following risk stratification with this assay, it would be anticipated that identification of HLA-B*5701 using molecular HLA typing methods would be required in <10% of the screened population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".