title: Reproducibility and experience dependence of echocardiographic indices of left ventricular function: Side-by-side comparison of global longitudinal strain and ejection fraction.
abstract: BACKGROUND
  Although left ventricular (LV) ejection fraction (EF) and global longitudinal strain (GLS) are recommended by the current echocardiographic chamber quantification guidelines, these measurements are not performed routinely. Because EF measurements rely on manual tracing of LV boundaries, and are subject to inter-reader variability and experience dependence, we hypothesized that semiautomated GLS measurements using speckle tracking would be more reproducible and less experience-dependent.
  
  
  METHODS
  Images from 30 patients were analyzed to obtain biplane EF using manual tracing. GLS was measured in three long-axis views using EchoInsight software (Epsilon Imaging) that automatically detects LV endocardial boundary, which is edited manually as necessary and is then automatically tracked throughout the cardiac cycle. All measurements were performed by an expert echocardiographer and three first-year cardiology fellows.
  
  
  RESULTS
  Semiautomated GLS analysis showed excellent correlation (r=.98) and small bias (-1.0±13% of measured value) between the experienced and less experienced readers, superior to EF (r=.91, bias 7.3±16%). Also, in repeated measurements, GLS showed higher intra-class correlation (ICC=.98) than EF (ICC=.89). Additionally, GLS analysis required ~1 minute per patient, while biplane EF measurements took twice as long.
  
  
  CONCLUSIONS
  Semiautomated GLS measurements are fast, less experience-dependent, and more reproducible than conventional EF measurements. This is probably because, irrespective of experience, the readers' choice of boundary position varies less when asked to refine the automated detection than to draw borders without initial clues. This technique may facilitate the workflow of a busy laboratory and make a step forward toward incorporating quantitative analysis into everyday echocardiography practice.
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Diego
authors.initials: D
authors.lastname: Medvedofsky
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Kalie
authors.initials: K
authors.lastname: Kebed
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Luke
authors.initials: L
authors.lastname: Laffin
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Stone
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Karima
authors.initials: K
authors.lastname: Addetia
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Roberto M
authors.initials: RM
authors.lastname: Lang
authors.affiliation: Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
authors.forename: Victor
authors.identifier: http://orcid.org/0000-0001-7932-6473
authors.initials: V
authors.lastname: Mor-Avi
mesh_terms: D000328:Adult; D002983:Clinical Competence / Q000706:statistics & numerical data*; D004452:Echocardiography / Q000379:methods*; D005260:Female; D006352:Heart Ventricles / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D008297:Male; D015203:Reproducibility of Results; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging* / Q000503:physiopathology
keywords: automated analysis; ejection fraction; left ventricular function; myocardial strain