PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
July 10, 2023Journal of the American Society of Echocardiography97 citationsOpen Access

State-of-the-Art: Noninvasive Assessment of Left Ventricular Function Through Myocardial Work

View Full Paper
AMAna MoyáDBDimitri BuytaertMPMartin Pěnička

Key Result

Noninvasive assessment of myocardial work using pressure-strain loop analysis provides a comprehensive evaluation of left ventricular mechanics and energetics across various cardiac pathologies.

Structured PICO

I
Intervention
Noninvasive assessment of myocardial work (MW) using pressure-strain loop analysis

Noninvasive myocardial work assessment via pressure-strain loop analysis offers a comprehensive evaluation of left ventricular mechanics and energetics across various cardiac pathologies.

Abstract

The assessment of myocardial work (MW) using noninvasive pressure-strain loop analysis is a novel echocardiographic method that provides a more precise assessment of cardiac performance by considering the left ventricular loading condition. By integrating various MW components such as index, efficiency, and constructive and wasted work, an extensive analysis of left ventricular mechanics and energetics can be achieved. This approach offers a more comprehensive assessment of global cardiac function and performance, surpassing conventional surrogate indices. In this review, we aim to summarize the existing knowledge on MW and its distinctive characteristics in various cardiac pathologies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Moyá et al. (2023) conducted a review in Cardiac pathologies. Noninvasive pressure-strain loop analysis for myocardial work (MW) vs. Conventional surrogate indices was evaluated. Noninvasive assessment of myocardial work using pressure-strain loop analysis provides a comprehensive evaluation of left ventricular mechanics and energetics across various cardiac pathologies.

synapsesocial.com/papers/6a0501b77d96552991e45df7https://doi.org/10.1016/j.echo.2023.07.002
Ask AI
Helpful
Bookmark
Share
View Full Paper