Digital twin integrating clinical, morphological and hemodynamic data to identify stroke risk factors

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  • dc.contributor.author Saiz-Vivó, Marta
  • dc.contributor.author Mill, Jordi
  • dc.contributor.author Iriart, Xavier
  • dc.contributor.author Cochet, Hubert
  • dc.contributor.author Piella Fenoy, Gemma
  • dc.contributor.author Sermesant, Maxime
  • dc.contributor.author Camara, Oscar
  • dc.date.accessioned 2025-10-21T05:43:10Z
  • dc.date.available 2025-10-21T05:43:10Z
  • dc.date.issued 2025
  • dc.description.abstract Stroke remains a leading global cause of mortality, with ischemic stroke as the most common subtype. Atrial fibrillation (AF) increases ischemic stroke risk due to thrombus formation in the left atrium (LA), particularly in the left atrial appendage (LAA). Traditional risk assessments, like the CHA2DS2-VASc score, focus on clinical factors but often overlook LA morphology and hemodynamics. Existing studies either use mechanistic models with limited cases or rely solely on clinical data, missing hemodynamic insights. This study integrates statistical and mechanistic models within a Digital Twin framework, using unsupervised Multiple Kernel Learning on 130 AF patients. Combining LA morphology, hemodynamics, and clinical data improved patient stratification, identifying three phenogroups. The highest-risk group exhibited larger atrial dimensions, complex LAA structures, and elevated B-type natriuretic peptide levels. This study underscores the potential of Digital Twin models in assessing thrombus risk, emphasizing the need for further research to refine stroke prediction models.en
  • dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreements No 101016496 (SimCardioTest) and No 101136438 (GEMINI), as well as by the Spanish Ministry of Science, Innovation and Universities under the GENERALITAAT grant, (PID2022-143239OB-I00). GP’s contribution was supported by the ICREA Academia program.en
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Saiz-Vivó M, Mill J, Iriart X, Cochet H, Piella G, Sermesant M, et al. Digital twin integrating clinical, morphological and hemodynamic data to identify stroke risk factors. NPJ Digit Med. 2025 Jun 17;8(1):369 . DOI: 10.1038/s41746-025-01676-1
  • dc.identifier.doi http://dx.doi.org/10.1038/s41746-025-01676-1
  • dc.identifier.issn 2398-6352
  • dc.identifier.uri http://hdl.handle.net/10230/71587
  • dc.language.iso eng
  • dc.publisher Nature Research
  • dc.relation.ispartof NPJ Digital Medicine. 2025 Jun 17;8(1):369
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101016496
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2022-143239OB-I00
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101136438
  • dc.rights This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
  • dc.subject.other Sistema cardiovascularca
  • dc.subject.other Imatges -- Processamentca
  • dc.subject.other Aprenentatge automàticca
  • dc.title Digital twin integrating clinical, morphological and hemodynamic data to identify stroke risk factorsen
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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