<i>HLA-B*58:01</i> Genotyping to Prevent Cases of DRESS and SJS/TEN in East Asians Treated with Allopurinol—A Canadian Missed Opportunity
Bibliographic record
Abstract
Background and objective:East Asians exposed to the urate-lowering drug allopurinol have a predilection for severe cutaneous drug reactions such as drug-induced hypersensitivity syndrome or drug reaction with eosinophilia and systemic symptoms (DRESS) and Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN). Screening is recommended in patients of East Asian descent for the presence of HLA-B*58:01 prior to allopurinol initiation to avoid these complications. Utilization rates of the HLA-B*58:01 predictive screening test within the Greater Vancouver area, which has a population composed of 40.1% people of East Asian descent, are unknown.Measures:We identified cases of DRESS or SJS/TEN due to allopurinol using the Vancouver General Hospital dermatology consult service database. We next compared the frequency in which the HLA-B*58:01 screening test was ordered since 2012 to the estimated frequency of new prescriptions for allopurinol prescribed for the management of gout among the East Asians.Results:We report 5 cases of East Asian patients exposed to allopurinol for management of gout between 2012 and 2016, who developed DRESS (4 patients) or SJS/TEN (1 patient). All were of HLA-B*58:01 genotype, representing preventable cases. The HLA-B*58:01 test was ordered 6 times in 2012, whereas the estimated number of new cases of allopurinol-prescribed gout among patients of East Asian descent during that time period was 13. For 2012, testing was ordered for only 46% of at-risk patients.Conclusion:We continue to observe cases of severe cutaneous drug reactions among high-risk individuals due to allopurinol exposure. The HLA-B*58:01 screening test for allopurinol hypersensitivity is underutilized in our geographic area.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".