Estimating Left Ventricular Elastance from Aortic Flow Waveform, Ventricular Ejection Fraction, and Brachial...

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Computational Modeling

Background:

• Left ventricular end-systolic elastance (Ees) is a key indicator of cardiac contractility.
• Current methods for Ees estimation are often invasive, limiting clinical application.

Purpose of the Study:

• To develop and validate a noninvasive method for estimating Ees.
• To enable accurate assessment of cardiac contractility using readily available measurements.

Main Methods:

• A two-step inverse method was developed using a validated 1D cardiovascular model.
• The method utilizes aortic flow and brachial pressure as inputs.
• Optimization algorithms were employed to tune arterial properties and estimate Ees parameters.

Main Results:

• The method demonstrated high agreement (r=0.99) and accuracy (n-RMSE=4%) in estimating Ees across 50 in silico cases.
• Sensitivity analysis showed minimal impact from arterial parameter adjustment and measurement errors (≤8% for Ees).

Conclusions:

• The developed noninvasive method provides accurate estimation of left ventricular end-systolic elastance.
• This technique holds potential for improved clinical assessment of cardiac contractility.