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Identification of novel signal of DRESS associated with antibiotics: a disproportionality analysis of the FDA adverse
Khushi Goyal1, Ruchika Sharma2, Ashok Kumar Datusalia3
1Department of Pharmaceutical Biotechnology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India.
Purpose:
Antibiotics are widely used in the management of bacterial infections However; most antibiotics are not known for DRESS. Our objective is to find out the association of DRESS with available antibiotics using disproportionality analysis.
Methods:
Retrospective pharmacovigilance disproportionality analysis based on the FDA Adverse Event Reporting System (FAERS) database from a period of 2004 Q1- 2022 Q3 was conducted using OpenVigil 2.1 tool. Disproportionality measures like Proportional reporting Ratio with associated Chi- square values (PRR ≥ 2 with associated χ2 ≥ 4), ROR with a 95% confidence interval (lower limit of 95% C.I. of ROR is greater than 1), and the number of cases of co-occurrence (n) were used for the identification of novel signals.
Results:
A total of 13,918 cases of DRESS were reported, out of which 5,455 cases were found with various classes of antibiotics. The signal of DRESS was identified with a total of 40 antibiotics. Sub groups analysis results have shown variation in the strength of signal based on gender, age groups and geographical locations. The sensitivity analysis results have shown a decrease in the strength of signal after removal of cases of concomitant drugs.
Conclusion:
22 antibiotics were identified which can be associated with DRESS; however, further causality assessment is required to confirm the association.
Insights
This study identified 22 antibiotics associated with Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS). Further research is needed to confirm these antibiotic-DRESS links.
Area of Science:
- Pharmacovigilance
- Drug Safety
- Adverse Drug Reactions
Background:
- Antibiotics are crucial for bacterial infections but rarely linked to DRESS.
- Understanding antibiotic-DRESS associations is vital for patient safety.
Purpose of the Study:
- To investigate the association between antibiotics and DRESS using disproportionality analysis.
- To identify specific antibiotics potentially linked to DRESS.
Main Methods:
- Retrospective pharmacovigilance analysis of the FAERS database (2004-2022).
- Utilized disproportionality measures including PRR and ROR to detect safety signals.
- Employed OpenVigil 2.1 tool for data analysis.
Main Results:
- Over 13,900 DRESS cases were reported; 5,455 involved antibiotics.
- Safety signals for DRESS were identified with 40 different antibiotics.
- Signal strength varied by demographics and decreased when concomitant drugs were excluded.
Conclusions:
- 22 antibiotics showed a potential association with DRESS.
- Further causality assessment is recommended to confirm these findings.
- This research aids in identifying high-risk antibiotics for DRESS.
