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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1622736

This article is part of the Research TopicAutoinflammatory novelties: from pathogenic mechanisms to clinical and therapeutic implicationsView all 12 articles

Drug-Induced Sweet's Syndrome: Pharmacovigilance Insights from FAERS with a Cross-Database Consistency Assessment in VigiBase via LASSO and Multivariable Logistic Regression

Provisionally accepted
Yuhan  XieYuhan Xie1Qinxiao  LiQinxiao Li2Jing  ZhouJing Zhou3Xiangqi  QinXiangqi Qin3Zhe  ZhangZhe Zhang3Ruimin  BaiRuimin Bai1*
  • 1First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
  • 2Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
  • 3Xi’an Jiaotong University, Xi'an, China

The final, formatted version of the article will be published soon.

Background: Drug-induced Sweet's syndrome (DISS), a rare but serious adverse drug reaction characterized by acute febrile neutrophilic dermatosis, remains difficult to identify due to its low incidence and diverse drug triggers. Methods: Drugs associated with DISS were systematically identified and characterized using data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS; Q1 2004– Q4 2024). Reports were analyzed for baseline characteristics, comorbidities, time-to-onset, drug class distributions, and polypharmacy patterns assessed through drug co-occurrence network analysis. Disproportionality analysis identified candidate drugs, which were refined using the least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression. The main analysis excluded malignancy-and/or immune-related indications, with two sensitivity analyses to assess robustness. A cross-database consistency assessment was conducted in VigiBase, supplemented by PubMed literature review and product label examination. Results: A total of 2,018 DISS cases involving 342 drugs were identified. The median time to onset was 22 (interquartile range: 7–98) days, with 55.6% occurring within 30 days. Ninety drugs demonstrated positive disproportionality signals; a similar pattern was observed in the subset of reports submitted by medical doctors. Of these, 24 remained significant in the main model (area under the curve = 0.815, 95% confidence interval: 0.775–0.856), predominantly comprising antineoplastic and anti-infective agents. Sensitivity analyses produced comparable results. Cross-database assessment in VigiBase identified overlap for 10 signals, while literature review supported associations for 15 drugs and 9 were documented as associated with SS in the product labels. Conclusion: This study provides a comprehensive evaluation of drugs associated with DISS using real-world pharmacovigilance data. The results reveal both established and previously unrecognized drug triggers, offering important insights to support early detection, clinical management, and improved drug safety monitoring from statistical and clinical perspectives.

Keywords: Drug-induced Sweet's syndrome, FAERS, VigiBase, Pharmacovigilance, Disproportionality analysis, LASSO regression, multivariable logistic regression

Received: 04 May 2025; Accepted: 03 Sep 2025.

Copyright: © 2025 Xie, Li, Zhou, Qin, Zhang and Bai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ruimin Bai, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

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