Trimethoprim/sulfamethoxazole and risk of haemophagocytic lymphohistiocytosis (HLH): a literature review and disproportionality analysis using individual case safety reports from FAERS
Bibliographic record
Abstract
OBJECTIVES: This study aimed to review existing literature regarding the association between haemophagocytic lymphohistiocytosis (HLH) and trimethoprim/sulfamethoxazole and examine the risk of HLH using data from the FDA Adverse Event Reporting System (FAERS). METHODS: We conducted a literature review to identify studies investigating the link between trimethoprim/sulfamethoxazole and HLH. Additionally, we performed an active-comparator restricted disproportionality analysis (ACR-DA) in FAERS (2004Q1-2023Q4) for trimethoprim/sulfamethoxazole compared to amoxicillin/clavulanate and azithromycin. We calculated reporting odds ratios (ROR), adjusted ROR (accounting for age category and indications for respiratory and urinary tract infections), proportional reporting ratios and information components (IC25), to identify potential safety signals for HLH. RESULTS: Our literature review identified five case reports of HLH linked to trimethoprim/sulfamethoxazole, showing a male predominance of 80% and a median age of 39 years. Symptoms appeared 2 to 14 days after initiating treatment, with fever being the first symptom reported in all patients. The disproportionality analysis indicated that reports of HLH associated with trimethoprim/sulfamethoxazole were approximately 3 to 14 times more frequent than those for amoxicillin/clavulanate (ROR, 3.08; 95% CI, 1.67-5.68) and azithromycin (ROR, 13.89; 95% CI, 5.88-32.82), with most reports submitted by other healthcare professionals. CONCLUSIONS: Trimethoprim/sulfamethoxazole may be linked to an increased risk of HLH. Further pharmacoepidemiologic research is necessary to confirm these findings and inform the safe clinical use of trimethoprim/sulfamethoxazole.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".