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. 2024 Mar 13;14:6089. doi: 10.1038/s41598-024-56304-x

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Machine learning of cardiovascular risk factors including the platelet lipidome facilitates sub-phenotyping and prediction of adverse events in patients with CAD. Workflow of this large-scale (n = 595) prospective study investigating the significance of the platelet lipidome to predict adverse thrombo-ischemic and bleeding events in patients with CAD by machine learning. The platelet lipidome in this study was assessed though an untargeted UHPLC-MS/MS assay. Alongside reliable risk parameters including platelet functional data, platelet lipids significantly contributed to risk prediction of adverse thrombo-ischemic and major bleeding events during the three-year clinical follow-up. CAR, acylcarnitines; LPE, lysophosphatidylethanolamines; UHPLC-MS/MS, Ultra-high performance liquid chromatography tandem mass spectrometry.