Unsupervised stratification of patients with myocardial infarction based on imaging and in-silico biomarkers
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916387942825984 |
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| author | Serra, Dolors Romero, Pau Franco, Paula Bernat, Ignacio Lozano, Miguel Garcia-Fernandez, Ignacio Soto, David Berruezo, Antonio Camara, Oscar Sebastian, Rafael |
| author_facet | Serra, Dolors Romero, Pau Franco, Paula Bernat, Ignacio Lozano, Miguel Garcia-Fernandez, Ignacio Soto, David Berruezo, Antonio Camara, Oscar Sebastian, Rafael |
| contents | This study presents a novel methodology for stratifying post-myocardial infarction patients at risk of ventricular arrhythmias using patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images. The method integrates imaging and computational simulation with a simplified cellular automaton model, Arrhythmic3D, enabling rapid and accurate VA risk assessment in clinical timeframes. Applied to 51 patients, the model generated thousands of personalized simulations to evaluate arrhythmia inducibility and predict VA risk. Key findings include the identification of slow conduction channels (SCCs) within scar tissue as critical to reentrant arrhythmias and the localization of high-risk zones for potential intervention. The Arrhythmic Risk Score (ARRISK), developed from simulation results, demonstrated strong concordance with clinical outcomes and outperformed traditional imaging-based risk stratification. The methodology is fully automated, requiring minimal user intervention, and offers a promising tool for improving precision medicine in cardiac care by enhancing patient-specific arrhythmia risk assessment and guiding treatment strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised stratification of patients with myocardial infarction based on imaging and in-silico biomarkers Serra, Dolors Romero, Pau Franco, Paula Bernat, Ignacio Lozano, Miguel Garcia-Fernandez, Ignacio Soto, David Berruezo, Antonio Camara, Oscar Sebastian, Rafael Computational Engineering, Finance, and Science Medical Physics This study presents a novel methodology for stratifying post-myocardial infarction patients at risk of ventricular arrhythmias using patient-specific 3D cardiac models derived from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images. The method integrates imaging and computational simulation with a simplified cellular automaton model, Arrhythmic3D, enabling rapid and accurate VA risk assessment in clinical timeframes. Applied to 51 patients, the model generated thousands of personalized simulations to evaluate arrhythmia inducibility and predict VA risk. Key findings include the identification of slow conduction channels (SCCs) within scar tissue as critical to reentrant arrhythmias and the localization of high-risk zones for potential intervention. The Arrhythmic Risk Score (ARRISK), developed from simulation results, demonstrated strong concordance with clinical outcomes and outperformed traditional imaging-based risk stratification. The methodology is fully automated, requiring minimal user intervention, and offers a promising tool for improving precision medicine in cardiac care by enhancing patient-specific arrhythmia risk assessment and guiding treatment strategies. |
| title | Unsupervised stratification of patients with myocardial infarction based on imaging and in-silico biomarkers |
| topic | Computational Engineering, Finance, and Science Medical Physics |
| url | https://arxiv.org/abs/2409.06526 |