A real-world study based on the FAERS database evaluating adverse drug reactions in three amphotericin B lipid formulations
Bibliographic record
Abstract
Background: Amphotericin B (AmB) remains the cornerstone in the treatment of severe fungal infections. However, selecting an appropriate lipid-based formulation for different clinical scenarios remains a challenge for clinicians and clinical pharmacists.Methods: Adverse event (AE) reports from the FDA Adverse Event Reporting System (FAERS) database (Q1 2004–Q3 2024) were retrospectively analysed to assess the safety profiles of three lipid formulations of AmB: liposomal amphotericin B (L-AmB), amphotericin B lipid complex (ABLC), and amphotericin B colloidal dispersion (ABCD). The baseline patient characteristics, AE distributions, and prognostic outcomes of severe AEs were examined. SPSS software was used to compare AE occurrences among the three groups.Results: A total of 3284 patient reports were included, comprising 3108 in the L-AmB group, 142 in the ABLC group, and 34 in the ABCD group. Within 30 days, AEs were reported in 666 cases (L-AmB), 72 cases (ABLC), and 13 cases (ABCD) (P < 0.001). AEs were categorised using the System Organ Class (SOC) and Standardized MedDRA Querie (SMQ). Compared with the L-AmB group, the ABLC group had a significantly higher incidence of hypersensitivity and hypertension; whereas hypokalemia was significantly lower (P < 0.001). Compared to the L-AmB and ABLC groups, the ABCD group had a significantly higher incidence of haematopoietic thrombocytopenia (P < 0.001). Prognostic analysis indicated that the incidence of life-threatening events was significantly higher in the ABCD group than in the L-AmB and ABLC groups (P < 0.001).Conclusion: The safety profiles of L-AmB, ABLC, and ABCD differ among organ systems. These findings highlight the need for individualised treatment strategies based on drug-specific safety characteristics and patient-specific clinical conditions to ensure optimal drug selection and patient safety.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".