{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:24:22Z","timestamp":1790267062542,"version":"4.1.0"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Natural Science Foundation","award":["2024JJA140326"],"award-info":[{"award-number":["2024JJA140326"]}]},{"name":"District-level Research Project","award":["Z-B20221458"],"award-info":[{"award-number":["Z-B20221458"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Drug-induced acute respiratory distress syndrome (ARDS) represents a severe adverse drug reaction with substantial implications for patient safety. The pathophysiology of ARDS is characterized by inflammatory cascades, endothelial dysfunction, and increased vascular permeability, culminating in pulmonary edema and compromised gas exchange. While prior studies have emphasized the impact of drug-induced adverse events (ADEs) in critical illness, comprehensive analyses leveraging large-scale databases remain underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed data from the FDA Adverse Event Reporting System (FAERS) database, covering the period from the first quarter of 2004 to the fourth quarter of 2024. The dataset underwent meticulous preprocessing, including deduplication and standardization of adverse event and drug nomenclature using the Medical Dictionary for Regulatory Activities (MedDRA) and WHO Drug Dictionaries. Disproportionality analyses were conducted using established metrics, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Chi-Square, Information Component (IC), and Empirical Bayes Geometric Mean (EBGM), to identify ADE signals. The final dataset comprised 18,613,992 patients, of whom 15,986 were identified as experiencing targeted ADEs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The findings revealed that drug-induced ARDS is associated with significant morbidity and mortality. Among the 15,986 patients with targeted ADEs, 46.69% were male and 42.81% were female, with a median age of 55 years. Notably, 26.01% of patients were aged 65 or older. The majority of reports originated from the United States (37.86%), followed by France (13.64%) and Japan (8.65%). Severe outcomes were prevalent, with 65.04% of patients requiring hospitalization, 51.21% resulting in death, and 30.64% classified as life-threatening. Time-to-onset analysis demonstrated that 17.23% of ADEs occurred within the first 30 days of drug administration, with a median onset time of 19 days.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study highlights the critical nature of drug-induced ARDS and underscores the necessity for continuous pharmacovigilance and timely intervention in ADE management. The findings provide valuable insights into the demographic and clinical profiles of patients experiencing targeted ADEs, emphasizing the importance of robust drug safety surveillance. Future research should focus on elucidating risk factors, underlying mechanisms, and long-term outcomes associated with ADEs to enhance patient safety and optimize clinical management strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0334500","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T18:28:03Z","timestamp":1764008883000},"page":"e0334500","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mining and analysis of reported ARDS population in FAERS database"],"prefix":"10.1371","volume":"20","author":[{"given":"Jun Hua","family":"Lai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi Ping","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi 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