Unintentional sulfonylurea toxicity due to a drug–drug interaction: a case report
Bibliographic record
Abstract
BACKGROUND: Sulfonylureas are widely used for type 2 diabetes mellitus, but these medications carry a risk of hypoglycemia. Drug-drug interactions that inhibit sulfonylurea metabolism and thus increase systemic exposure can cause unintentional sulfonylurea toxicity. CASE PRESENTATION: A 56-year-old man presented with severe, recurrent hypoglycemia. He had a history of type 2 diabetes mellitus and was taking the sulfonylurea gliclazide with no prior episodes of hypoglycemia. The onset of his hypoglycemia occurred within days after starting voriconazole and subsequently fluconazole for a fungal pneumonia. Unintentional sulfonylurea toxicity developed due to an adverse drug-drug interaction between gliclazide and these antifungals. Azole antifungals inhibit the metabolism of sulfonylureas resulting in increased systemic exposure and consequent toxicity. After the diagnosis of sulfonylurea toxicity was recognized, the patient was treated initially with dextrose and then administered octreotide to prevent recurrent hypoglycemia. He was successfully managed, his hypoglycemic episodes resolved, and his medications were adjusted to avoid any further adverse interactions. CONCLUSIONS: Adverse drug-drug interactions continue to pose challenges to clinicians. Both individual vigilance and system wide strategies are needed to prevent and mitigate consequences. This case highlights an important drug-drug interaction and reviews the presentation, management and antidotal therapy of sulfonylurea toxicity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".