Unraveling the genetic link: an umbrella review on HLA-B*15:02 and antiepileptic drug-induced Stevens–Johnson syndrome/toxic epidermal necrolysis
Bibliographic record
Abstract
PURPOSE: This umbrella review was conducted to summarize the association between HLA*1502 allele with antiepileptic induced Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). METHODS: Pubmed, Scopus and EMBASE were searched for eligible reviews in May 2023. Two authors independently screened titles and abstracts and assessed full-text reviews for eligibility. The quality of meta-analyses and case-control studies was appraised with Assessing the Methodological Quality of Systematic Reviews 2 and Newcastle-Ottawa Scale, respectively. Narrative summaries of each antiepileptic drug were analyzed. Preestablished protocol was registered on the International Prospective Register of Systematic Reviews Registry(ID: CRD42023403957). RESULTS: Included studies are systematic reviews, meta-analyses and case-control studies evaluating the association of HLA-B*1502 allele with the following antiepileptics. Seven meta-analyses for carbamazepine, three meta-analyses for lamotrigine (LTG), three case-control studies for oxcarbazepine, nine case-control studies for phenytoin and four case-control studies for phenobarbitone were included. The findings of this umbrella review suggest that there is a strong association between HLA-B-1502 with SJS/TEN for carbamazepine and oxcarbazepine and a milder association for lamotrigine and phenytoin. CONCLUSION: In summary, although HLA-B*1502 is less likely to be associated with phenytoin or lamotrigine-induced SJS/TEN compared to carbamazepine-induced SJS/TEN, it is a significant risk factor that if carefully screened, could potentially reduce the development of SJS/TEN. In view of potential morbidity and mortality, HLA-B*1502 testing may be beneficial in patients who are initiating lamotrigine/phenytoin therapy. However, further studies are required to examine the association of other alleles with the development of SJS/TEN and to explore the possibility of genome-wide association studies before initiation of treatment.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".