Abstract

Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08–1.16, p < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10, p < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693, p = 0.801) and GCS (auto 0.668 vs. manual 0.686, p = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04–1.15, p < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.

Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312.

Details

Title
Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction
Author
Backhaus, Sören J. 1 ; Aldehayat, Haneen 2 ; Kowallick, Johannes T. 3 ; Evertz, Ruben 1 ; Lange, Torben 1 ; Kutty, Shelby 4 ; Bigalke, Boris 5 ; Gutberlet, Matthias 6 ; Hasenfuß, Gerd 2 ; Thiele, Holger 7 ; Stiermaier, Thomas 8 ; Eitel, Ingo 8 ; Schuster, Andreas 1 

 Georg-August-University Göttingen, Department of Cardiology and Pneumology, University Medical Centre, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210); German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237) 
 Georg-August-University Göttingen, Department of Cardiology and Pneumology, University Medical Centre, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210) 
 German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237); Georg-August University, University Medical Center Göttingen, Institute for Diagnostic and Interventional Radiology, Göttingen, Germany (GRID:grid.7450.6) (ISNI:0000 0001 2364 4210) 
 The Johns Hopkins Hospital and School of Medicine, Helen B. Taussig Heart Center, Baltimore, USA (GRID:grid.411935.b) (ISNI:0000 0001 2192 2723) 
 University Medical Center Berlin, Department of Cardiology, Charité Campus Benjamin Franklin, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662) 
 Heart Center Leipzig at University of Leipzig, Institute of Diagnostic and Interventional Radiology, Leipzig, Germany (GRID:grid.9647.c) (ISNI:0000 0004 7669 9786) 
 Heart Center Leipzig at University of Leipzig, Department of Internal Medicine/Cardiology, Leipzig, Germany (GRID:grid.9647.c) (ISNI:0000 0004 7669 9786) 
 University Hospital Schleswig-Holstein, University Heart Center Lübeck, Medical Clinic II (Cardiology/Angiology/Intensive Care Medicine), Lübeck, Germany (GRID:grid.412468.d) (ISNI:0000 0004 0646 2097); German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2691268157
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.