{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T22:10:16Z","timestamp":1767046216006,"version":"3.41.2"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"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. Pharmacol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Sacubitril\/valsartan is extensively used in heart failure; however, there are few long-term safety studies of it in a wide range of populations. The aim of this study was to evaluate sacubitril\/valsartan-induced adverse events (AEs) through data mining of the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Reports in the FAERS from the third quarter of 2015 (FDA approval of sacubitril\/valsartan) to the fourth quarter of 2023 were collected and analyzed. Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms were adopted in data mining to quantify signals of sacubitril\/valsartan-associated AEs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 12,001,275 reports of sacubitril\/valsartan as the \u201cprimary suspected (PS)\u201d and 99,651 AEs induced by sacubitril\/valsartan were identified. More males than females reported AEs (59.95% vs. 33.31%), with the highest number of reports in the 60\u201370\u00a0years age group (8.11%), and most AEs occurred &amp;lt; 7\u00a0days (14.13%) and \u2265 60\u00a0days (10.69%) after dosing. Sacubitril\/valsartan-induced AE occurrence targeted 24 system organ classes (SOCs) and 294 preferred terms (PTs). Of these, 4 SOCs were strongly positive for all four algorithms, including cardiac disorders, vascular disorders, ear and labyrinth disorders, and respiratory, thoracic and mediastinal disorders. Among all PTs, consistent with the specification, hypotension (n = 10,078) had the highest number of reports, and dizziness, cough, peripheral swelling, blood potassium increased, and renal impairment were also reported in high numbers. Notably, this study also discovered a high frequency of side effects such as death, dyspnea, weight change, feeling abnormal, hearing loss, memory impairment, throat clearing, and diabetes mellitus.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study identified potential new AE signals and gained a more general understanding of the safety of sacubitril\/valsartan, promoting its rational adoption in the cardiovascular system.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2024.1392263","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T20:34:08Z","timestamp":1723754048000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A real-world disproportionality analysis of sacubitril\/valsartan: data mining of the FDA adverse event reporting system"],"prefix":"10.3389","volume":"15","author":[{"given":"Yiwen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuna","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s002280050466","article-title":"A Bayesian neural network method for adverse drug reaction signal generation","volume":"54","author":"Bate","year":"1998","journal-title":"Eur. 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