Backhaus, Sören Jan

[["dc.bibliographiccitation.artnumber","oeac053"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","European Heart Journal Open"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Backhaus, Sören J"],["dc.contributor.author","Rösel, Simon F"],["dc.contributor.author","Stiermaier, Thomas"],["dc.contributor.author","Schmidt-Rimpler, Jonas"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Kowallick, Johannes T"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Bigalke, Boris"],["dc.contributor.editor","Gimelli, Alessia"],["dc.date.accessioned","2022-11-01T10:16:55Z"],["dc.date.available","2022-11-01T10:16:55Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\n \n Aims\n Deformation imaging enables optimized risk prediction following acute myocardial infarction (AMI). However, costly and time-consuming post processing has hindered widespread clinical implementation. Since manual left-ventricular long-axis strain (LV LAS) has been successfully proposed as a simple alternative for LV deformation imaging, we aimed at the validation of left-atrial (LA) LAS.\n \n \n Methods and results\n The AIDA STEMI and TATORT-NSTEMI trials recruited 795 patients with ST-elevation myocardial infarction and 440 with non-ST-elevation myocardial infarction. LA LAS was assessed as the systolic distance change between the middle of a line connecting the origins of the mitral leaflets and either a perpendicular line towards the posterior atrial wall (LAS90) or a line connecting to the LA posterior portion of the greatest distance irrespective of a predefined angle (LAS). Primary endpoint was major adverse cardiac event (MACE) occurrence within 12 months. There were no significant differences between LA LAS and LAS90, both with excellent reproducibility. LA LAS correlated significantly with LA reservoir function (Es, r = 0.60, P < 0.001). Impaired LA LAS resulted in higher MACE occurrence [hazard ratio (HR) 0.85, 95% confidence interval (CI) 0.82–0.88, P < 0.001]. LA LAS (HR 0.90, 95% CI 0.83–0.97, P = 0.005) and LV global longitudinal strain (GLS, P = 0.025) were the only independent predictors for MACE in multivariate analyses. C-statistics demonstrated incremental value of LA LAS in addition to GLS (P = 0.016) and non-inferiority compared with FT Es (area under the receiver operating characteristic curve 0.74 vs. 0.69, P = 0.256).\n \n \n Conclusion\n Left-atrial LAS provides fast and software-independent approximations of quantitative LA function with similar value for risk prediction compared with dedicated deformation imaging.\n \n \n Clinical trial registration\n ClinicalTrials.gov: NCT00712101 and NCT01612312"],["dc.identifier.doi","10.1093/ehjopen/oeac053"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/116687"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-605"],["dc.relation.eissn","2752-4191"],["dc.title","Left-atrial long-axis shortening allows effective quantification of atrial function and optimized risk prediction following acute myocardial infarction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Aldehayat, Haneen"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Bigalke, Boris"],["dc.contributor.author","Gutberlet, Matthias"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Thiele, Holger"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-09-01T09:50:07Z"],["dc.date.available","2022-09-01T09:50:07Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\n \n 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,\n p\n  < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10,\n p\n  < 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,\n p\n  = 0.801) and GCS (auto 0.668 vs. manual 0.686,\n p\n  = 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,\n p\n  < 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.\n \n Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312."],["dc.description.sponsorship"," Deutsches Zentrum für Herz-Kreislaufforschung http://dx.doi.org/10.13039/100010447"],["dc.description.sponsorship"," Georg-August-Universität Göttingen 501100003385"],["dc.identifier.doi","10.1038/s41598-022-16228-w"],["dc.identifier.pii","16228"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113627"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","2045-2322"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

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