{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:34:19Z","timestamp":1791999259476,"version":"4.4.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Drug-induced Sweet\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2013Q4 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 2,018 DISS cases involving 342 drugs were identified. The median time to onset was 22 (interquartile range: 7\u201398) days, with 55.60% 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\u20130.856), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1622736","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:35:55Z","timestamp":1758638155000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Drug-induced Sweet\u2019s syndrome: pharmacovigilance insights from FAERS with a cross-database consistency assessment in VigiBase via LASSO and multivariable logistic 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