Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment
Abstract This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clini...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-59114-3 |
| _version_ | 1797199549550297088 |
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| author | Jorge Corral Acero Pablo Lamata Ingo Eitel Ernesto Zacur Ruben Evertz Torben Lange Sören J. Backhaus Thomas Stiermaier Holger Thiele Alfonso Bueno-Orovio Andreas Schuster Vicente Grau |
| author_facet | Jorge Corral Acero Pablo Lamata Ingo Eitel Ernesto Zacur Ruben Evertz Torben Lange Sören J. Backhaus Thomas Stiermaier Holger Thiele Alfonso Bueno-Orovio Andreas Schuster Vicente Grau |
| author_sort | Jorge Corral Acero |
| collection | DOAJ |
| description | Abstract This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors (P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771—0.777) vs. 0.683 (0.681—0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment. |
| first_indexed | 2024-04-24T07:17:31Z |
| format | Article |
| id | doaj.art-7e50cbc306084863b721a1c6fd0d4354 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:17:31Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7e50cbc306084863b721a1c6fd0d43542024-04-21T11:14:32ZengNature PortfolioScientific Reports2045-23222024-04-0114111410.1038/s41598-024-59114-3Comprehensive characterization of cardiac contraction for improved post-infarction risk assessmentJorge Corral Acero0Pablo Lamata1Ingo Eitel2Ernesto Zacur3Ruben Evertz4Torben Lange5Sören J. Backhaus6Thomas Stiermaier7Holger Thiele8Alfonso Bueno-Orovio9Andreas Schuster10Vicente Grau11Department of Engineering Science, Institute of Biomedical Engineering, University of OxfordDepartment of Digital Twins for Healthcare, School of Biomedical Engineering and Imaging Sciences, King’s College LondonMedical Clinic II, Cardiology, Angiology and Intensive Care Medicine, University Heart Centre LübeckDepartment of Engineering Science, Institute of Biomedical Engineering, University of OxfordDepartment of Cardiology and Pneumology, University Medical Centre Göttingen, Georg-August UniversityDepartment of Cardiology and Pneumology, University Medical Centre Göttingen, Georg-August UniversityDepartment of Cardiology, Campus Kerckhoff of the Justus-Liebig-University Giessen, Kerckhoff-ClinicMedical Clinic II, Cardiology, Angiology and Intensive Care Medicine, University Heart Centre LübeckDepartment of Internal Medicine/Cardiology and Leipzig Heart Science, Heart Centre Leipzig at University of LeipzigDepartment of Computer Science, University of OxfordDepartment of Cardiology and Pneumology, University Medical Centre Göttingen, Georg-August UniversityDepartment of Engineering Science, Institute of Biomedical Engineering, University of OxfordAbstract This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors (P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771—0.777) vs. 0.683 (0.681—0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment.https://doi.org/10.1038/s41598-024-59114-3 |
| spellingShingle | Jorge Corral Acero Pablo Lamata Ingo Eitel Ernesto Zacur Ruben Evertz Torben Lange Sören J. Backhaus Thomas Stiermaier Holger Thiele Alfonso Bueno-Orovio Andreas Schuster Vicente Grau Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment Scientific Reports |
| title | Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment |
| title_full | Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment |
| title_fullStr | Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment |
| title_full_unstemmed | Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment |
| title_short | Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment |
| title_sort | comprehensive characterization of cardiac contraction for improved post infarction risk assessment |
| url | https://doi.org/10.1038/s41598-024-59114-3 |
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