{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:36:35Z","timestamp":1784097395815,"version":"3.55.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"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. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Perioperative cerebrovascular thromboembolic events are serious complications of stent-assisted embolization (SAE) for unruptured intracranial aneurysms (UIAs). To date, there have been no definitive clinical trial results to effectively predict and prevent the occurrence of this complication. This study aims to elucidate the correlation between platelet aggregation rate (PAR) and thromboembolic events (TEs), with the goal of predicting the occurrence of cerebrovascular TEs in these patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this retrospective, single-center cohort study, we included 704 cases of unruptured intracranial aneurysms treated with stent-assisted intervention from 2016 to 2020. Cerebrovascular TEs were defined as cerebral ischemic events occurring within 7\u202fdays before or after the interventional procedure. Light Transmission Aggregometry (LTA) was used to detect PAR in patients. Clinical data, including patients\u2019 demographic information and perioperative PAR, were collected. Multivariate analysis was conducted to examine the correlation between these factors and the occurrence of TEs. Additionally, Lasso regression was employed to select clinical indicators associated with perioperative TEs. Receiver Operating Characteristic (ROC) curves were generated for prognostic indicators such as PAR, with the optimal cutoff value determined. A nomogram was then simulated, and predictive accuracy of the model was evaluated using Decision Curve Analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 562 patients were included in the final analysis. Significant differences were observed in the incidence of thrombosis between the control group and the experimental group (9.38% vs. 4.96%). The ROC curve of platelet aggregation index, highly correlated with prognosis and derived from Lasso regression, identified the optimal cutoff value for the maximum preoperative PAR as 19.81. A nomogram was constructed based on selected clinical baseline data, and its calibration was assessed using data from the prediction group. The net benefit of the experimental group model\u2019s DCA curve was significantly improved.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>For patients undergoing SAE for UIAs, utilizing PAR and other indicators as reference standards for treatment results in better prognosis compared to empirical treatment based on guidelines. Guiding antiplatelet therapy using PAR and other indicators is both meaningful and beneficial to clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1538753","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T04:14:35Z","timestamp":1746072875000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Platelet aggregation rate serves as a significant predictive indicator for thromboembolic events in the context of stent-assisted embolization for unruptured arterial aneurysms"],"prefix":"10.3389","volume":"16","author":[{"given":"Xiaopeng","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingbao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.3171\/2023.2.JNS222919","article-title":"Trends 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