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Antibiotic-associated adverse events in bone and joint infections: a FAERS pharmacovigilance study
Haoping Dai1, Hongtao Li1, Changming Xiao1
1Department of Spine Surgery, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China.
Background:
Bone and joint infections (BJI) require prolonged antibiotic therapy that may amplify cumulative toxicity risk, yet indication-contextualized safety data remain sparse.
Methods:
Using the FDA Adverse Event Reporting System (FAERS, 2004Q1-2025Q4; 20,006,981 deduplicated cases), we identified 9,072 cases involving 17 BJI-target antibiotics. Four disproportionality measures, namely reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), and empirical Bayes geometric mean (EBGM), were applied against the full FAERS database. We then performed BJI within-cohort contextualization, primary-suspect-only (PS-only) sensitivity analysis, exploratory XGBoost machine learning, and Weibull time-to-onset (TTO) modeling, in accordance with the READUS-PV reporting framework.
Results:
Of 153 drug-adverse drug event (ADE) pairs, 103 met the prespecified four-method robust full-FAERS signal criterion; 28 were retained after BJI within-cohort contextualization. Indication-supported signals included vancomycin-nephrotoxicity (within-cohort ROR 3.30, 95% CI 2.95-3.70), linezolid-hematologic toxicity (3.65, 3.21-4.16), ceftaroline-hematologic toxicity (4.81, 3.42-6.77), and ertapenem-neurotoxicity (4.76, 3.50-6.47). Several strong full-FAERS signals (e.g., fluoroquinolone-tendon and broad-spectrum-C. difficile infection) were attenuated within the BJI cohort or under PS-only restriction, highlighting the role of prescribing pattern and reporting-role confounding. Weibull modeling indicated predominantly early-onset reporting patterns.
Conclusion:
Multi-method, indication-contextualized FAERS analysis distinguishes BJI-supported from general reporting signals and provides a transparent framework for prioritizing monitoring during prolonged antibiotic therapy. Findings are hypothesis-generating and require validation in controlled observational studies. They prioritize safety signals rather than providing estimates of incidence, absolute risk, causality, dose-response, or renal-function effects.
Insights
This study analyzed FDA adverse event reports for bone and joint infections (BJI) antibiotics. It identified specific drug-adverse event pairs, highlighting the need for contextualized safety monitoring during prolonged antibiotic therapy.
Area of Science:
- Pharmacovigilance
- Infectious Diseases
- Drug Safety
Background:
- Bone and joint infections (BJI) necessitate prolonged antibiotic treatment.
- Cumulative toxicity risks associated with these therapies are significant.
- Contextualized safety data for BJI treatments remain limited.
Purpose of the Study:
- To identify and contextualize adverse drug events (ADEs) associated with antibiotics used for BJI.
- To differentiate between general reporting signals and those specific to BJI treatment.
- To establish a framework for prioritizing safety monitoring in BJI therapy.
Main Methods:
- Utilized the FDA Adverse Event Reporting System (FAERS) database (2004-2025).
- Analyzed 9,072 cases involving 17 BJI-target antibiotics using four disproportionality measures.
- Applied BJI within-cohort contextualization, primary-suspect-only analysis, machine learning, and time-to-onset modeling.
Main Results:
- Identified 28 robust drug-ADE pairs after BJI contextualization.
- Confirmed signals include vancomycin-nephrotoxicity, linezolid-hematologic toxicity, ceftaroline-hematologic toxicity, and ertapenem-neurotoxicity.
- Demonstrated attenuation of some general signals (e.g., fluoroquinolone-tendon) within the BJI cohort, indicating confounding factors.
Conclusions:
- Multi-method, indication-contextualized analysis effectively distinguishes BJI-specific safety signals.
- The findings provide a transparent framework for prioritizing safety monitoring during prolonged antibiotic therapy for BJI.
- Results are hypothesis-generating and require validation through further observational studies.
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