{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:12:43Z","timestamp":1788966763850,"version":"build-2803163510"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81703759"],"award-info":[{"award-number":["81703759"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> Ticagrelor and clopidogrel are commonly used antiplatelet agents, and we conducted a pharmacovigilance analysis using the Food and Drug Administration Adverse Event Reporting System (FAERS) to provide a reference for safe and reasonable clinical use.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Data were collected in FAERS from 2012 Q1 to 2022 Q2 for data cleaning. We used system organ classes (SOCs) and prefer terms (PTs) from the Medical Dictionary of Regulatory Activity (MedDRA version 25.1). Adverse event reports were retrieved at the PT level. Adverse reaction (ADR) signals of ticagrelor and clopidogrel were mined by calculating reporting odds ratios (ROR), proportional reporting ratios (PRR), information component (IC) and empirical Bayesian geometric mean (EBGM). After that, further analysis of the hemorrhagic signals and their clinical information were performed.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The number of ADR reports where the primary suspect (PS) drugs were 15,133 for ticagrelor and 23,860 for clopidogrel. Significant ADR signals were identified by the SOC analysis for ticagrelor including cardiac disorders (ROR 4.87, PRR 4.46), respiratory disorders (ROR 2.45, PRR 2.28), and vascular disorders (ROR 2.22, PRR 2.16). Clopidogrel included blood disorders (ROR 2.86, PRR 2.77), vascular disorders (ROR 2.71, PRR 2.61), and cardiac disorders (ROR 2.29, PRR 2.22). At the PT level, the more frequent ADR signals for ticagrelor were dyspnoea, contusion, and haemorrhage, while clopidogrel were gastrointestinal haemorrhage, anaemia, and drug interaction. The hemorrhagic signals of both were mainly focused on the SOC level of gastrointestinal disorders, injury disorders and vascular disorders and nervous system disorders. The death and life-threatening rate of ticagrelor was 7.76 percentage higher than that of clopidogrel.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Clinicians need to pay attention to not only common ADRs but also be alert to new ADR signals when choosing to use ticagrelor and clopidogrel. This study provides a reference for the reasonable and safe clinical use of ticagrelor and clopidogrel.<\/jats:p>","DOI":"10.3389\/fphar.2022.970066","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T04:52:47Z","timestamp":1664772767000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Adverse reaction signals mining and hemorrhagic signals comparison of ticagrelor and clopidogrel: A pharmacovigilance study based on FAERS"],"prefix":"10.3389","volume":"13","author":[{"given":"Shu","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanshen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,10,3]]},"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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