ArticleScientific reports2025
Assessment of a non-invasive approach for pressure volume loop prediction in mice.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Left ventricular pressure-volume (PV) loop measurement provides insights into cardiac function by mapping the relationship between left ventricular pressure and volume throughout the cardiac cycle. PV loops are used extensively in pre-clinical studies, but they are obtained through invasive and often terminal procedures. In prior studies utilizing data from large mammals (e.g., pigs and humans), quantitative models have been utilized to estimate PV loops utilizing non-invasive measurements. These studies rely on three key assumptions: constant normalized elastance (ratio of pressure over volume) across individuals, accurate non-invasive estimation of left ventricular systolic and diastolic pressure, and non-invasive estimation of left ventricular volume data. Here we assessed the application and suitability of these approaches to mice, a smaller pre-clinical animal. We found limitations of this approach due to variability in elastance among mice, suggesting that the constancy observed in larger animals might not generally extend to rodents. Additionally, while non-invasive estimates of end systolic pressures were close to invasively obtained measurements, they were not reliable for determining maximum elastance. Moreover, although individual correlations between invasive and non-invasive volume measurements were strong, creating a generalized predictive model of pressure-volume loop proved challenging due to variability across sample data set.
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