Which is the best Myocardial Work index for the prediction of coronary artery disease? A data meta-analysis.


Journal

Echocardiography (Mount Kisco, N.Y.)
ISSN: 1540-8175
Titre abrégé: Echocardiography
Pays: United States
ID NLM: 8511187

Informations de publication

Date de publication:
03 2023
Historique:
revised: 24 12 2022
received: 24 06 2022
accepted: 20 01 2023
pubmed: 8 2 2023
medline: 15 3 2023
entrez: 7 2 2023
Statut: ppublish

Résumé

Early diagnosis of Coronary Artery Disease (CAD) plays a key role to prevent adverse cardiac events such as myocardial infarction and Left Ventricular (LV) dysfunction. Myocardial Work (MW) indices derived from echocardiographic speckle tracking data in combination with non-invasive blood pressure recordings seems promising to predict CAD even in the absence of impairments of standard echocardiographic parameters. Our aim was to compare the diagnostic accuracy of MW indices to predict CAD and to assess intra- and inter-observer variability of MW through a meta-analysis. Electronic databases were searched for observational studies evaluating the MW indices diagnostic accuracy for predicting CAD and intra- and inter-observer variability of MW indices. Pooled sensitivity, specificity, and Summary Receiver Operating Characteristic (SROC) curves were assessed. Five studies enrolling 501 patients met inclusion criteria. Global Constructive Work (GCW) had the best pooled sensitivity (89%) followed by GLS (84%), Global Work Index (GWI) (82%), Global Work Efficiency (GWE) (80%), and Global Wasted Work (GWW) (75%). GWE had the best pooled specificity (78%) followed by GWI (75%), GCW (70%), GLS (68%), and GWW (61%). GCW had the best accuracy according to SROC curves, with an area under the curve of 0.86 compared to 0.84 for GWI, 0.83 for GWE, 0.79 for GLS, and 0.74 for GWW. All MW indices had an excellent intra- and inter-observer variability. GCW is the best MW index proving best diagnostic accuracy in the prediction of CAD with an excellent reproducibility.

Sections du résumé

BACKGROUND
Early diagnosis of Coronary Artery Disease (CAD) plays a key role to prevent adverse cardiac events such as myocardial infarction and Left Ventricular (LV) dysfunction. Myocardial Work (MW) indices derived from echocardiographic speckle tracking data in combination with non-invasive blood pressure recordings seems promising to predict CAD even in the absence of impairments of standard echocardiographic parameters. Our aim was to compare the diagnostic accuracy of MW indices to predict CAD and to assess intra- and inter-observer variability of MW through a meta-analysis.
METHODS
Electronic databases were searched for observational studies evaluating the MW indices diagnostic accuracy for predicting CAD and intra- and inter-observer variability of MW indices. Pooled sensitivity, specificity, and Summary Receiver Operating Characteristic (SROC) curves were assessed.
RESULTS
Five studies enrolling 501 patients met inclusion criteria. Global Constructive Work (GCW) had the best pooled sensitivity (89%) followed by GLS (84%), Global Work Index (GWI) (82%), Global Work Efficiency (GWE) (80%), and Global Wasted Work (GWW) (75%). GWE had the best pooled specificity (78%) followed by GWI (75%), GCW (70%), GLS (68%), and GWW (61%). GCW had the best accuracy according to SROC curves, with an area under the curve of 0.86 compared to 0.84 for GWI, 0.83 for GWE, 0.79 for GLS, and 0.74 for GWW. All MW indices had an excellent intra- and inter-observer variability.
CONCLUSIONS
GCW is the best MW index proving best diagnostic accuracy in the prediction of CAD with an excellent reproducibility.

Identifiants

pubmed: 36748264
doi: 10.1111/echo.15537
doi:

Types de publication

Meta-Analysis Journal Article Comment

Langues

eng

Sous-ensembles de citation

IM

Pagination

217-226

Commentaires et corrections

Type : CommentOn

Informations de copyright

© 2023 Wiley Periodicals LLC.

Références

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Auteurs

Antonio Parlavecchio (A)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Giampaolo Vetta (G)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Rodolfo Caminiti (R)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Manuela Ajello (M)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Michele Magnocavallo (M)

Department of Clinical, Internal, Anesthesiology and Cardiovascular Sciences, Policlinico Universitario Umberto I, Sapienza University of Rome, Rome, Italy.

Francesco Vetta (F)

Arrhythmology Unit, Paideia Hospital, Rome, Italy.

Rosario Foti (R)

San Vincenzo Hospital, Taormina, Italy.

Pasquale Crea (P)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Antonio Micari (A)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Scipione Carerj (S)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Domenico Giovanni Della Rocca (DG)

Texas Cardiac Arrhythmia Institute, St. David's Medical Center, Austin, Texas, USA.
Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing, Universitair Ziekenhuis Brussel-Vrije Universiteit Brussel, European Reference Networks Guard-Heart, Brussels, Belgium.

Gianluca Di Bella (G)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

Concetta Zito (C)

Department of Clinical and Experimental Medicine, Cardiology Unit, University of Messina, Messina, Italy.

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