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Introduction <p>Eosinophilia encompasses a wide spectrum of hematologic and non-hematologic disorders, and may lead to clinically significant organ damage. Although numerous medications have been implicated in eosinophilia and related hypersensitivity reactions, their overall drug-associated risk remains unclear. Real-world pharmacovigilance data can provide important insights into rare but serious eosinophilia-related adverse events (AEs). This study investigated the demographic and drug-related risk factors for eosinophilia-associated AEs using a large dataset from the US Food and Drug Administration Adverse Event Reporting System (FAERS).</p> Aim <p>To comprehensively evaluate the associations between a broad range of medications and eosinophilia-related AEs.</p> Method <p>FAERS reports from 2004 Q1 to 2025 Q1 were analyzed. Four disproportionality algorithms—reporting odds ratio, proportional reporting ratio, Multi-item Gamma Poisson Shrinker, and Bayesian Confidence Propagation Neural Network—were applied to identify drugs with significant safety signals. LASSO regression was used for variable selection, followed by a multivariate logistic regression analysis. The Bonferroni correction was applied for multiple comparisons. The time to onset of eosinophilia-related AEs was evaluated.</p> Results <p>In total, 55,456 eosinophilia-related AEs were identified. Using all four disproportionality methods, 173 drugs were significantly associated with eosinophilia. These included anti-infective medications (82/173), cardiovascular agents (20/173), nervous system drugs (16/173), antineoplastic and immunomodulatory therapies (14/173), digestive system drugs (9/173), respiratory system drugs (9/173) and other medications (23/173). LASSO followed by multivariate logistic regression identified 60 drugs that were independently associated with higher reporting odds within the FAERS database along with several demographic characteristics. Male sex and age 56–69&#xa0;years were also significantly associated with higher reporting odds (adjusted <i>p</i> &lt; 0.01). Among reports with available timelines, the median time to onset of eosinophilia-related AEs was 20&#xa0;days (interquartile range 7–44&#xa0;days).</p> Conclusion <p>This large pharmacovigilance study identified the key demographic and drug-related risk factors for eosinophilia-associated AEs. These findings highlight the need for increased clinical vigilance, especially during the early weeks of therapy and among patients with identified risk factors. Although causality cannot be established from the FAERS data, the results offer valuable real-world evidence to support the early detection, risk mitigation, and post-marketing safety monitoring of medications associated with eosinophilia.</p>

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Eosinophilia-related adverse events reporting associated with medications: a disproportionality and regression analysis of the FDA Adverse Event Reporting System

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Haimo Liu,
  • Xingang Li,
  • Yiqi Sun,
  • Siyang Song,
  • Sheng Cheng,
  • Yin Liao,
  • Weina Wang,
  • Fang Yi

摘要

Introduction

Eosinophilia encompasses a wide spectrum of hematologic and non-hematologic disorders, and may lead to clinically significant organ damage. Although numerous medications have been implicated in eosinophilia and related hypersensitivity reactions, their overall drug-associated risk remains unclear. Real-world pharmacovigilance data can provide important insights into rare but serious eosinophilia-related adverse events (AEs). This study investigated the demographic and drug-related risk factors for eosinophilia-associated AEs using a large dataset from the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Aim

To comprehensively evaluate the associations between a broad range of medications and eosinophilia-related AEs.

Method

FAERS reports from 2004 Q1 to 2025 Q1 were analyzed. Four disproportionality algorithms—reporting odds ratio, proportional reporting ratio, Multi-item Gamma Poisson Shrinker, and Bayesian Confidence Propagation Neural Network—were applied to identify drugs with significant safety signals. LASSO regression was used for variable selection, followed by a multivariate logistic regression analysis. The Bonferroni correction was applied for multiple comparisons. The time to onset of eosinophilia-related AEs was evaluated.

Results

In total, 55,456 eosinophilia-related AEs were identified. Using all four disproportionality methods, 173 drugs were significantly associated with eosinophilia. These included anti-infective medications (82/173), cardiovascular agents (20/173), nervous system drugs (16/173), antineoplastic and immunomodulatory therapies (14/173), digestive system drugs (9/173), respiratory system drugs (9/173) and other medications (23/173). LASSO followed by multivariate logistic regression identified 60 drugs that were independently associated with higher reporting odds within the FAERS database along with several demographic characteristics. Male sex and age 56–69 years were also significantly associated with higher reporting odds (adjusted p < 0.01). Among reports with available timelines, the median time to onset of eosinophilia-related AEs was 20 days (interquartile range 7–44 days).

Conclusion

This large pharmacovigilance study identified the key demographic and drug-related risk factors for eosinophilia-associated AEs. These findings highlight the need for increased clinical vigilance, especially during the early weeks of therapy and among patients with identified risk factors. Although causality cannot be established from the FAERS data, the results offer valuable real-world evidence to support the early detection, risk mitigation, and post-marketing safety monitoring of medications associated with eosinophilia.