Hasenfuß, Gerd P.

[["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"]]

[["dc.bibliographiccitation.firstpage","1540"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Diabetes"],["dc.bibliographiccitation.lastpage","1548"],["dc.bibliographiccitation.volume","69"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Stiermaier, Thomas"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Navarra, Jenny-Lou"],["dc.contributor.author","Koschalka, Alexander"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Gutberlet, Matthias"],["dc.contributor.author","Thiele, Holger"],["dc.contributor.author","Eitel, Ingo"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2021-04-14T08:25:11Z"],["dc.date.available","2021-04-14T08:25:11Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.2337/db20-0001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81545"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1939-327X"],["dc.relation.issn","0012-1797"],["dc.title","Cardiac Magnetic Resonance Myocardial Feature Tracking for Optimized Risk Assessment After Acute Myocardial Infarction in Patients With Type 2 Diabetes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1580"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","European Journal of Heart Failure"],["dc.bibliographiccitation.lastpage","1587"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Emami, Amir"],["dc.contributor.author","Saitoh, Masakazu"],["dc.contributor.author","Valentova, Miroslava"],["dc.contributor.author","Sandek, Anja"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Ebner, Nicole"],["dc.contributor.author","Loncar, Goran"],["dc.contributor.author","Springer, Jochen"],["dc.contributor.author","Doehner, Wolfram"],["dc.contributor.author","Lainscak, Mitja"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Anker, Stefan D."],["dc.contributor.author","von Haehling, Stephan"],["dc.date.accessioned","2020-12-10T14:06:15Z"],["dc.date.available","2020-12-10T14:06:15Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1002/ejhf.1304"],["dc.identifier.issn","1388-9842"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69830"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Comparison of sarcopenia and cachexia in men with chronic heart failure: results from the Studies Investigating Co-morbidities Aggravating Heart Failure (SICA-HF)"],["dc.title.alternative","Comparison of sarcopenia and cachexia in men with chronic HF"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Journal of Interventional Cardiology"],["dc.bibliographiccitation.lastpage","9"],["dc.bibliographiccitation.volume","2022"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Beuthner, Bo Eric"],["dc.contributor.author","Topci, Rodi"],["dc.contributor.author","Toischer, Karl"],["dc.contributor.author","Puls, Miriam"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.editor","Kim, Michael C."],["dc.date.accessioned","2022-06-01T09:39:37Z"],["dc.date.available","2022-06-01T09:39:37Z"],["dc.date.issued","2022"],["dc.description.abstract","Background. Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS). Methods. Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed. Results. Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943–0.997) p = 0.032 ; automated: HR 0.967 (95% CI 0.939–0.995) p = 0.022 ) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938–0.999) p = 0.043 ; automated: HR 0.963 [95% CI 0.933–0.995] p = 0.024 ; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920–0.996) p = 0.027 ; automated: HR 0.954 (95% CI 0.920–0.989) p = 0.011 ). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p = 0.21 ). Absolute values of LV volumes differed significantly between automated and manual approaches ( p < 0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient. Conclusion. Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR."],["dc.identifier.doi","10.1155/2022/1368878"],["dc.identifier.pii","1368878"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/108521"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-572"],["dc.relation.eissn","1540-8183"],["dc.relation.issn","0896-4327"],["dc.title","Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","104334"],["dc.bibliographiccitation.journal","eBioMedicine"],["dc.bibliographiccitation.volume","86"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Uzun, Harun"],["dc.contributor.author","Rösel, Simon F."],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Crawley, Richard J."],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-12-01T08:32:00Z"],["dc.date.available","2022-12-01T08:32:00Z"],["dc.date.issued","2022"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/100010447 Deutsches Zentrum für Herz-Kreislaufforschung"],["dc.identifier.doi","10.1016/j.ebiom.2022.104334"],["dc.identifier.pii","S2352396422005163"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118336"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.issn","2352-3964"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Hemodynamic force assessment by cardiovascular magnetic resonance in HFpEF: A case-control substudy from the HFpEF stress trial"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]