Your browser doesn't support javascript.
loading
A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.
Zingaro, Alberto; Ahmad, Zan; Kholmovski, Eugene; Sakata, Kensuke; Dede', Luca; Morris, Alan K; Quarteroni, Alfio; Trayanova, Natalia A.
Afiliação
  • Zingaro A; ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA.
  • Ahmad Z; MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
  • Kholmovski E; ELEM Biotech S.L., Pier07, Via Laietana, 26, 08003, Barcelona, Spain.
  • Sakata K; ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA.
  • Dede' L; Department of Applied Mathematics and Statistics, Johns Hopkins University, 100 Wyman Park Dr, 21211, Baltimore, MD, USA.
  • Morris AK; ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA.
  • Quarteroni A; Department of Radiology, University of Utah, 30 N Mario Capecchi Dr., 84112, Salt Lake City, UT, USA.
  • Trayanova NA; ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA.
bioRxiv ; 2024 Jan 15.
Article em En | MEDLINE | ID: mdl-38293150
ABSTRACT
Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article