<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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".